Journal of Industrial Information Integration 33 (2023) 100470
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Journal of Industrial Information Integration
journal homepage: www.sciencedirect.com/journal/journal-of-industrial-information-integration
Review article
State-of-the-art AI-based computational analysis in civil engineering
Chen Wang a, Ling-han Song a, Zhou Yuan a, Jian-sheng Fan a, b, *
a
b
Key Laboratory of Civil Engineering Safety and Durability of China Education Ministry, Department of Civil Engineering, Tsinghua University, Beijing, 100084, China
Beijing Engineering Research Center of Steel and Concrete Composite Structures, Tsinghua University, Beijing, 100084, China
A R T I C L E I N F O
A B S T R A C T
Keywords:
Civil engineering
Artificial intelligence
Computational analysis
Research review
Machine learning
Deep learning
With the informatization of the building and infrastructure industry, conventional analysis methods are grad­
ually proving inadequate in meeting the demands of the new era, such as intelligent synchronization and realtime simulation. Artificial intelligence (AI) technology has emerged as a promising alternative due to its high
expressiveness, efficiency, and scalability. This has given rise to a new research field of AI-based computation in
civil engineering. In this study, a state-of-the-art review of the research on material and structural analyses using
AI technology in civil engineering was performed to provide a general introduction to the current progress. The
research was classified into static feature studies, dynamic feature studies, and composite feature studies ac­
cording to the problem inputs. The general methodology, commonly used AI models, and representative ap­
plications of each research category were elaborated. On these bases, the strengths and weaknesses of current
studies were discussed. To demonstrate the accuracy and efficiency of AI models in comparison with conven­
tional numerical methods, a concrete example of an end-to-end deep learning framework for structural analysis
was highlighted. Finally, we suggested four open problems from the perspective of engineering applications,
indicating the major challenges and future research directions regarding AI-based computational analysis in civil
engineering.
1. Introduction
In recent years, the rise of digital technologies, such as the concept of
the digital twin [1,2], has led to an increased interest in the development
of modern computational techniques across many engineering areas.
This has prompted a pursuit of more intelligent and synchronized
methods for simulating physical entities in the virtual world [3].
In the field of civil engineering, computational analysis of con­
struction materials and structures serves as a fundamental technique
throughout the building and infrastructure industry lifecycle, and plays
a crucial role in connecting reality and virtuality. While numerical
computing techniques, especially finite element analysis (FEA), have
significantly enhanced the complexity of computable problems in en­
gineering practice [4–7], they are not without limitations. Firstly, the
accuracy of the computation results depend on the underlying consti­
tutive models and their numerical implementation algorithms [8–11],
which are interdisciplinary, requiring significant research effort and
time to develop appropriate models. Secondly, the computational effi­
ciency of such approaches is inadequate for large-scale structures or
complex analyses involving high-dimensional elements [12,13]. For
instance, simulating the elastoplastic hysteretic response of a steel plate
shear wall structure with shell elements can take several hours [14],
which places a heavy time burden on parametric analysis in engineering
design. Thirdly, the transferability and scalability of models across
different platforms are restricted. Despite numerous commercial pack­
ages offering interactive interfaces, the quality of computation results is
influenced by the user’s familiarity with the platform, limiting the
dissemination and refinement of good models across different com­
mercial software. In short, classic numerical methods still require sig­
nificant manual effort and are falling short of the new-era requirements
in terms of accuracy, efficiency, openness, and generality, leading to an
urgent need for more advanced information technologies to achieve
substantial improvements.
From the perspective of phenomenological modeling [15–17],
emerging artificial intelligence (AI) technologies [18–21], especially
machine learning (ML) and deep learning (DL) techniques, are prom­
ising alternatives that provide new possibilities for breaking through
empirical cognition and eliminating manual intervention. ML, including
DL, is capable of learning latent patterns directly from data and
approximating arbitrary continuous functions that map input spaces to
* Corresponding author at: Department of Civil Engineering, Tsinghua University, Haidian District, Beijing, China.
E-mail address: fanjsh@tsinghua.edu.cn (J.-s. Fan).
https://doi.org/10.1016/j.jii.2023.100470
Available online 5 May 2023
2452-414X/© 2023 Published by Elsevier Inc.
C. Wang et al.
Journal of Industrial Information Integration 33 (2023) 100470
output spaces [22–24]. Furthermore, the increase in the computing
power supported by graphics processing units (GPUs) [25] and tensor
processing units (TPUs) [26,27] endows these new intelligent models
with significantly improved computational efficiency over traditional
methods after deployment. In addition, most state-of-the-art AI models
are developed on open-source platforms in the Python language [28,29],
making them highly scalable and transferable. These advantages of AI
have catalyzed new research paradigms, accelerating research processes
in many engineering disciplines [30,31] and helping to discover new
scientific knowledge. Accordingly, an increasing number of scholars are
exploring the applications of AI technologies in the computational
analysis of construction materials and structures, which leads to a new
research hotspot of AI-based computational analysis in civil engineering.
Research on the applications of AI in other areas in civil engineering,
such as structural health monitoring [32] and construction management
[33,34], started early and has yielded a wealth of successful explorative
experience [35–39]. Many review studies have summarized critical
techniques and prospected future development [40–45]. However, the
field of AI-based computational analysis in civil engineering is still in its
infancy. Adeli [46] briefly reviewed early neural networks in structural
engineering and their applications in intelligent modeling of structural
materials and design optimization. Tapeh and Naser [47] classified and
summarized the applications of AI technologies (including ML and DL)
in structural engineering based on different scenarios, such as earth­
quake and wind engineering. This review provided numerous specific
AI-based computing examples and conducted scientometric analysis.
However, the review did not further refine the methodology of intelli­
gent computing, and there were many overlaps in the technical routes
among different scenarios, with only changes in the predicted targets.
Therefore, this emerging research direction still lacks systematic orga­
nization and analysis. A review that can distill general methodology of
AI-based computing will benefit researchers and engineers by providing
insight into current research progress and inspiring future academic
explorations and engineering applications.
This study provides a review of AI-based computational research
related to material and structural analysis in civil engineering. The
remainder of this paper is organized as follows. Section 2 demonstrates
the rationality of applying AI to the field of computational analysis in
civil engineering and identifies key aspects used to classify the subse­
quent review. Section 3 introduces the literature search and selection
criteria. On this basis, the current state-of-the-art research is reviewed in
Sections 4~6 by summarizing the general research methodology,
overviewing commonly used AI algorithms, and presenting representa­
tive applications. In Section 7, the current progress is analyzed, espe­
cially about the limitations of the existing research. We introduce a
recent study on end-to-end DL-based structural analysis in Section 8,
which provides a concrete example to address the present limitations
and demonstrate the performance of AI models. Finally, in Section 9,
four open problems for future research on AI-based computational
analysis in civil engineering are proposed.
models typically involve selecting key points on response curves based
on experimental results, such as the yield point and the ultimate loading
capacity point of each cycle. Parametric analysis is then performed to
identify correlations between the key points and intrinsic features of
structures, such as geometric information and construction configura­
tions. Finally, appropriate functions are constructed to describe the
curve shape and cyclic behavior connecting neighboring key points.
Examples of macroscopic models include the reinforced concrete (RC)
coupling beam model proposed by Ding et al. [48] and the steel brace
model proposed by Liu et al. [49]. The mathematical mechanism of the
macroscopic models is function fitting based on human experience and
experimental observations, without strong mechanical restrictions. The
material-based elaborate models are further subdivided into constitutive
models based on artificial laws and constitutive models within the
classic elastoplasticity framework. The former follow modeling pro­
cedures similar to those of macroscopic models, such as constitutive
studies on concrete [50,51]. The latter are more abstract, which corre­
spond to an optimization problem with the principle of maximum plastic
dissipation as the objective function [8]:
max σ : ε˙p + q⋅α̇
σ,q
s.t.
(1)
,
f (σ, q) ≤ 0
where f(σ, q) is the yield criterion; q represents the internal variable
vectors; and α denotes the conjugate variables of q. Assuming that the
yield criterion is convex, which can be satisfied by most construction
materials in civil engineering, the Lagrange dual problem of the above
optimization problem is
max inf L (σ, q, λ) = − σ : ε˙p − q⋅α̇ + λf (σ, q)
σ,q
,
s.t.
(2)
λ≥0
where λ is the Lagrangian multiplier. Then, the Karush-Kuhn-Tucker
(KKT) conditions [52] for this problem are
⎧
⎪
∂f
⎪
⎪
⎪ ∇σ L (σ, q, λ) = 0⇒ εp ⋅ = λ
⎨
∂σ
.
(3)
⎪
∂f
⎪
⎪
⎪
⎩ ∇q L (σ, q, λ) = 0⇒α̇ = λ ∂q
Accordingly, the associative flow rule, which is widely adopted in
classic elastoplasticity, belongs to the KKT conditions, and the plastic
flow increment λ, which is critical in numerical implementations such as
the return mapping algorithm [53,54], is the Lagrangian multiplier of
the dual problem. Eqs. (1)~(3) indicate that the yield criterion and the
hardening laws that control the evolution of internal variables can all be
manipulated. Most classic elastoplastic constitutive models use mathe­
matical regression to formulate special hardening laws that reproduce
coupon test results [55]. For instance, the elastoplastic model of struc­
tural steel proposed in [56] developed the external Chaboche-Voce
combined hardening framework and the internal "incomplete collapse
effect of the memory surface" concept based on experimental observa­
tions that the trends of cyclic hardening or softening resembled expo­
nential functions. In summary, the mathematical mechanism of most
classic computational models in civil engineering can be attributed to
phenomenological regression. Therefore, applying AI technologies with
powerful regressive capabilities [22,23] to replace artificial functions is
theoretically feasible and reasonable, which is anticipated to yield
significantly improved model performance.
In addition, the previous discussion reveals that all computational
models in civil engineering involve the handling of two key aspects: (1)
the intrinsic properties of the simulated objects, such as material
strengths and structural geometric parameters; and (2) the external
stimuli, mainly in the form of different loads and loading protocols. The
intrinsic properties, which we refer to as static features, primarily affect
2. Rationale for applying AI
Despite increasing studies on the application of AI models to
computational analysis in civil engineering, the underlying rationale has
not been fully demonstrated. In this section, we aim to address this gap
by revisiting classic numerical methods and deriving the mathematical
logic for incorporating AI into the field of computational analysis in civil
engineering. This will provide a scientific basis for the use of AI and help
establish appropriate classification criteria for subsequent literature
reviews.
Classic numerical studies in civil engineering focused on three levels
of application scenarios, the construction material level, the structural
member level, and the structural system level. These studies can be
broadly divided into two categories: member-/system-based macro­
scopic models and material-based elaborate models. The macroscopic
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Journal of Industrial Information Integration 33 (2023) 100470
the basic mechanical properties (such as the initial stiffness and ultimate
loading capacity) and control the shapes of full-range responses (such as
the pinching effect) [57–59]. On the other hand, the external stimuli,
referred to as dynamic features, govern loading and unloading conditions
in full-range responses and induce strong path-dependent effects in
nonlinear analyses [60,61]. These two types of features, as well as their
synthesis, require different modeling methods and derive distinctive
technical routes. Therefore, the subsequent review classifies the current
research into three major categories: static feature studies, dynamic
feature studies, and composite feature studies.
(5) Quality assessment: To ensure the inclusion of only high-quality
research, the selected articles were assessed according to the
Journal Citation Reports service, and mainly those published in
journals with a ranking above Q2 were chosen.
Following these literature search and selection procedures, 150 ar­
ticles were used in this review, which were presented in the Appendix
Table A1. Furthermore, quantitative analyses were performed to reveal
the trends of this emerging research area, which are presented in the
subsequent sections.
3. Literature search and selection
4. Static feature studies
This study performed sample collection of peer-reviewed articles
from well-accepted academic databases. The search and selection
criteria are summarized as follows:
From this section, state-of-the-art studies on the AI-based computa­
tional analysis of construction materials and structures in civil engi­
neering are systematically investigated. The studies are classified into
three categories based on the nature of their problem inputs: static
feature studies, dynamic feature studies, and composite feature studies.
For each category, we summarize the general methodology, introduce
commonly used intelligent algorithms, and present typical applications
at all three levels (i.e., construction materials, structural members, and
structural systems).
(1) Literature databases for search: Web of Science, Science Direct,
Scopus, ASCE Library, Wiley Online Library, Engineering Village,
and ProQuest.
(2) Keyword selection: The keywords were divided into two groups.
The first group was within the scope of artificial intelligence, such
as “artificial intelligence”, “machine learning”, “deep learning”,
“neural network”, etc. The second group was within the scope of
civil engineering, such as “civil engineering”, “structural engi­
neering”, “seismic performance”, “loading capacity”, “deforma­
tion”, etc. By combining words or phrases from the two groups (e.
g., "deep learning in structural engineering"), we generated
search terms to identify relevant articles. This led to a large pool
of candidate articles for selection.
(3) Inclusion criteria: To be included in our review, articles had to
focus on the computational analysis of materials or structures in
civil engineering and predict at least one aspect of their perfor­
mance or response, such as material strength or structural
deformation patterns, through the use of AI models. Articles that
explored AI methods in other fields of civil engineering, such as
the use of convolutional neural networks to identify the crack
width of concrete structures in structural diagnosis and mainte­
nance, were excluded.
(4) Screening process: The initial screening process involved check­
ing the titles, abstract and keywords of candidate articles to
identify duplicates, while the introduction and conclusions were
analyzed to ensure that the selected articles aligned with the re­
view objective. Next, we read the entire article and compiled
relevant information (e.g., title, source, year of publication, au­
thors, prediction targets, algorithms, and input data) in an Excel
file for organization.
4.1. General research pipelines of static feature studies
In general, static feature studies focus on problems where classic
analytical solutions are not feasible, or where numerous variables make
it challenging to identify quantitative relationships between inputs and
outputs. The inputs of these studies are typically the intrinsic properties
of construction materials and structures, such as material constituents
and structural construction configurations, which remain almost
invariant during the loading processes. The outputs are scalar mechan­
ical indices, such as the shear capacities of concrete structures or the
strengths of fiber-reinforced materials (as shown in Fig. 1). Conven­
tionally, the design formulas of these mechanical indices were derived
by mathematically fitting the results from large-scale FE parametric
analysis [62–64], wherein the FE models were validated by experiments.
However, static feature studies using AI technology substitute manually
fitted equations with ML or DL models, leading to more expressive and
accurate results. Moreover, the computational efficiency of intelligent
models allows for easy distinction of the significance of each input
variable through sensitivity analysis.
The general research pipeline of current static feature studies can be
summarized as follows.
(1) Establish a dataset with sufficient data by experiments or FE
analysis.
Fig. 1. Illustration of static feature studies.
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Journal of Industrial Information Integration 33 (2023) 100470
(2) Determine the input features according to the researchers’
experience or domain-specific knowledge.
(3) Select appropriate AI models and train them with the dataset.
(4) Compare the predicted results with empirical equations or cur­
rent codes.
(5) Perform sensitivity analysis to reveal the significance of each
input variable.
4.2. AI models in static feature studies
The AI models commonly used in static feature studies are analyzed,
and their distribution is displayed in Fig. 2. To better illustrate their
applications in the following sections, popular AI models are overviewed
for a fundamental technical background.
Artificial neural networks (ANNs), the most commonly-used algo­
rithm in static feature studies, consist of multilayer nodes (also called
neurons) and their interconnections [65], as shown in Fig. 3(a). Each
connection between two nodes at the neighboring layer represents a
linear transformation of the signal:
(
)
x(l+1) = ϕ wT(l+1) x(l) + b(l+1) ,
(4)
Fig. 2. Statistical distribution of intelligent algorithms in static feature studies.
(LR: logistic regression; LNR: linear or nonlinear regression; KNN: K-nearest
neighbors; SVM: support vector machine; DT: decision tree; ANN: artificial
neural network; DL: other deep learning models except ANNs).
where the subscripts l and l + 1 denote layer numbers; x is the set of
neural nodes; w and b are the weight and bias of the linear trans­
formation, respectively; and ϕ is an activation function that infuses
nonlinearity into ANNs, such as the rectified linear unit (ReLU) [66] and
Fig. 3. Illustration of commonly-used intelligent algorithms in static feature studies.
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Journal of Industrial Information Integration 33 (2023) 100470
the sigmoid function. By aggregating multiple nonlinear layers, ANNs
can approximate any continuous function [24].
Support vector machines (SVMs) [67] are a class of classic supervised
ML models that can be applied to both classification tasks and regression
tasks [68]. In applications, SVMs typically map the given data to a
high-dimensional space via kernel functions and search for a hyperplane
to separate the data with the maximal margin in classification tasks or a
band to encompass the data with the minimal margin in regression tasks
(Fig. 3(b)). The learning process can be abstracted as a convex optimi­
zation problem. For instance, in regression tasks, training an SVM cor­
responds to solving
min
w
s.t.
1
‖ w ‖2
2
,
|yi − 〈w, ϕ(xi )〉 − b| ≤ ε
4.3. Typical applications of static feature learning
Table 1 presents a selection of representative applications in static
feature studies across all three scenarios (construction materials, struc­
tural members, and structural systems) in recent years (mainly since
2018). For a comprehensive list, please refer to Table A1 in the
Appendix.
At the construction material level, the mechanical properties of
various concrete types with different additives have been the focal point
of static feature studies for decades [106–109]. Getahun et al. [73]
utilized an ANN to predict the 28-day strength of concrete incorporating
construction debris and agricultural waste. Ly et al. [81] trained an ANN
on 233 experimental data to estimate the compressive strength of rubber
concrete based on the input mixture, achieving high accuracy. Yang
et al. [75] leveraged an RF to predict the dynamic increase factor (DIF)
of steel-fiber-reinforced concrete (SFRC) with the strain rate, matrix
strength, fiber dosage, and fiber properties. Based on the trained model,
a sensitivity analysis was performed to identify the parameter with the
greatest influence on the DIF of SFRC. Chen et al. [82] compared the
performance of various algorithms, including ANN, SVM, boosting, and
RF, to investigate the bond strength between carbon fiber-reinforced
polymer (CFRP) and steel, finding that the boosting algorithm ach­
ieved the best accuracy. The learned models were further interpreted via
a parametric analysis.
At the structural member level, most relevant studies have adopted a
similar methodology to that used at the material level, with a focus on
predicting the mechanical performance of various structural compo­
nents [86–91]. Sirca Jr. and Adeli [104] utilized neural networks to
predict the uplift load capacity of metal roof panels, achieving sub­
stantially more accurate results than the conventional method. Sarve­
ghadi et al. [105] estimated the shear strength of SFRC beams using
multi-expression programming, which outperformed several equations
in the literature. Mangalathu et al. [85] investigated the failure modes
and shear capacities of RC beam-column joints using several ML algo­
rithms, such as logistic regression (LR), Lasso regression, K-nearest
neighbors (KNN), and RF, to identify the best approach. Naser [87]
explored the temporal responses of RC structures under fire conditions
by incorporating heating time as one of the input features in an ANN,
transforming the target problem into a series of static feature learning
processes. In recent years, as composite structures have gained popu­
larity, AI models have also been applied to predict the mechanical
properties of such structures. For instance, ML models have been used to
predict the capacity of CFST [93–95] and fiber-reinforced RC structures
[92,96].
(5)
where (xi, yi) is a training data tuple; and ε is the threshold.
The decision tree (DT) algorithm, which continuously classifies the
attributes of sample data in a top-down manner as shown in Fig. 3(c),
evolved from the field of decision analysis. The key step is to decide the
attribute node to generate new branches. For example, classification and
regression trees (CARTs) [69] are nonparametric DTs that can be applied
to both discrete and continuous attributes, which utilize the Gini index
as the branching criterion:
Gini(D|A) =
n
∑
|Di |
i=1
|D|
Gini(D),
(6)
where A is the attribute candidate; D is the set of sample data to be split;
Di is the subset of D when A takes the ith value; |D| denotes the size of the
dataset; and Gini(D) describes the “purity” of the dataset:
)2
n (
∑
|Ck |
Gini(D) = 1 −
,
(7)
|D|
k=1
where Ck is the subset of data that belong to the kth class. At each iter­
ation, the CART selects the attribute with the minimal Gini value and
splits the dataset into two parts.
To enhance the expressiveness of individual algorithms, ensemble
learning techniques [70] integrate multiple ML models with different
strategies, among which bagging and boosting are the two most popular
methods. Bagging, represented by the random forest (RF, Fig. 3(d)) al­
gorithm, generates a random data subset with replacement from the
entire database (called a bootstrapped dataset) for each decision tree
model, and each subset is trained separately. During the test stage, the
global prediction is obtained by aggregating the individual prediction of
each decision tree with specified rules such as average pooling. In
contrast to treating all samples equally, a boosting algorithm (Fig. 3(e))
recurrently adjusts the weights of incorrectly predicted data and uses
them to train a new underlying model (called a weak learner). By
incrementally fitting the residual bias, boosting converts a series of weak
learners into a strong learner with high accuracy. Typically, the boosting
algorithm performs better than the bagging algorithm but is more likely
to overfit the data.
According to Fig. 2, ANNs and classic ML algorithms, such as SVM
and boosting, are preferred in static feature studies, while advanced DL
models are seldom used. This can be attributed to two factors. First,
ANNs and classic ML algorithms are integrated into many scientific
computing packages, such as MATLAB and Scikit-learn [71], which offer
convenient function interfaces and do not require secondary develop­
ment or architectural innovation. Consequently, they have lower
learning costs and application thresholds. Second, the volume of data in
civil engineering is not abundant. As illustrated in Fig. 4, nearly 80% of
datasets collected in static feature studies are smaller than 1000, which
makes it challenging to train a DL model fully and may lead to over­
fitting problems that harm generalization capabilities.
Fig. 4. Statistical distribution of the data sizes used in static feature studies.
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Journal of Industrial Information Integration 33 (2023) 100470
Table 1
Typical applications of static feature learning.
Construction material
level
Structural member level
Structural system level
Author
Year
Task
Algorithm
Behnood et al. [72]
Getahun et al. [73]
Bui et al. [74]
Yang et al. [75]
Abueidda et al. [76]
Feng et al. [77]
Kaloop et al. [78]
Zhang et al. [79]
2018
2018
2018
2019
2019
2020
2020
2021
ANN
ANN
ANN
RF
CNN
boosting
boosting
RF
Salimbahrami et al.
[80]
Ly et al. [81]
Chen et al. [82]
Kang et al. [83]
Naderpour et al. [84]
Mangalathu et al. [85]
Alwanas et al. [86]
Naser [87]
Keshtegar et al. [88]
Abambres et al. [89]
Zhang et al. [90]
Feng et al. [91]
Kaveh et al. [92]
Vu et al. [93]
Zarringol et al. [94]
Chou et al. [95]
Alam et al. [96]
Morfidis et al. [97]
Sun et al. [98]
Huang et al. [99]
Hwang et al. [100]
2021
Compressive strength of silica fume concrete
Strength of concrete containing agricultural and construction wastes
Compressive and tensile strength of high performance concrete (HPC)
Dynamic increase factor for steel fiber-reinforced concrete (SFRC)
Mechanical properties of composite
Compressive strength of concrete
Compressive strength of HPC
Bond strength of near-surface-mounted fiber-reinforced polymer (FRP) bonded
to concrete
Compressive strength of recycled aggregate concrete
2021
2021
2021
2018
2018
2019
2019
2019
2020
2020
2021
2021
2021
2021
2022
2022
2017
2019
2019
2021
Compressive strength of rubber concrete
CFRP-steel bond strength
Compressive and flexural strength of SFRC
Shear resistance of concrete beams reinforced by FRP bars
Failure modes and shear strengths of RC beam-column joints
Load-carrying capacity and mode failure of beam-column joint connection
Responses of RC beams and columns in extreme conditions
Shear strength of steel fiber-unconfined RC beams
Shear capacities of one-way slabs under concentrated loads
Shear capacities of RC deep beams
Plastic hinge lengths of RC columns
Buckling loads of fiber steering composite cylinders
Concentric load capacities of concrete-filled steel tube (CFST) columns
Ultimate strength of CFCFST
Axial compression capacity of CFST
Shear strength of FRP-reinforced concrete members
Maximal interstory drift of RC frames
Maximal interstory drift of RC frames
In-plane failure modes for RC frames with infills
Maximal interstory drift and failure modes of RC frames
2021
2021
2021
Seismic drift estimation for steel frames
Buckling loads of imperfect reticulated shells
Holistic performance of RC frames
ANN
ANN, SVM, DT, boosting, RF
KNN, LNR, SVM, ANN, boosting
ANN
LR, LNR, KNN, DT, RF, SVM, etc.
ANN
ANN
SVM
ANN
SVM, ANN, boosting
boosting
RF, DT, LNR, ANN
boosting
SVM, ANN
ANN
ANN
ANN
LNR, SVM
boosting, DT, LR, ANN, RF, SVM
LNR, DT, RF, KNN, RF, boosting,
etc.
RF
ANN, SVM
LNR, KNN, SVM, boosting, RF
Guan et al. [101]
Zhu et al. [102]
Esteghamati et al.
[103]
Applications at the structural system level have mainly focused on
regular structures that can be characterized by finite features. Guan et al.
[101] utilized building information (such as the number of stories,
number of bays, and bay width), fundamental analysis information from
conventional FE nonlinear analysis (such as the first four modal shapes
and periods, the yield force and the associated drift in the pushover
analysis), and spectral intensity parameters to estimate the story drifts of
steel special moment resisting frames with an RF. This study innova­
tively augmented the input features with results from FE analysis, which
injected heuristic knowledge into ML, thus successfully enhancing the
model performance. Zhu et al. [102] predicted the nonlinear buckling
loads of single-layer reticulated shells considering imperfections by ANN
and SVM according to the height-to-span ratios, numbers of rings, and
boundary conditions. Esteghamati et al. [103] developed an ML pipeline
to assess the performance of mid-rise RC frame buildings, extending the
prediction target to include realistic impacts and providing a practical
approach for evaluating seismic vulnerability and environmental
behavior in the early design stage. As this study revealed, the assessment
of engineering structures can be abstracted as a multi-objective regres­
sion problem, enabling a methodology similar to that used in static
feature studies.
ANN, SVM
perspective, the prediction of the full-range response of a material or
structure specimen corresponds to a regression problem between two
sequences, such as the hysteretic load-displacement curves [110].
Accordingly, the AI models that cope with sequence analysis can be
applied to dynamic feature learning, which derive two research
branches with regard to the ranges of the input sequences of interest.
The first research branch focuses on predicting future responses
based on past responses of a single sequence (as illustrated in Fig. 5),
which aligns with standard time series analysis. The general research
pipeline for this branch can be summarized as follows.
(1) Select a response sequence and divide it into a training interval
and a test interval.
(2) Divide the training interval into multiple pieces of data, each
comprising an input segment and a target segment.
(3) Train an intelligent model with the data pieces from the training
interval.
(4) Validate the accuracy of the trained model using the test interval.
The first branch of dynamic feature research has limited practical
applications because the trained models cannot generalize to new
stimuli. For practical engineering purposes, models that can simulate
responses under different loading cases are required. Hence, the second
branch of dynamic feature research aims to predict the full-range re­
sponses of construction materials or engineering structures under new
stimuli by learning the entire response sequences in the training set (as
illustrated in Fig. 6). This branch aligns with mainstream AI domains
and is similar to sequence tasks in natural language processing (NLP)
[111], such as machine translation, in which AI models can translate
new sentences from the source language to the target language after
learning the given corpus. Similarly, the general research pipeline of
5. Dynamic feature studies
5.1. General research pipelines of dynamic feature studies
Dynamic feature studies aim to predict the full-range responses of a
construction material or structure specimen under various external
stimuli. In contrast to static features such as structural geometries, dy­
namic features, which mainly manifest in loading protocols, exhibit
more randomness, such as the earthquake stimuli. From a mathematical
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branch, any time series analysis model can be employed, making ANNs a
popular choice due to their simplicity. However, for the second branch
that predicts full-range responses of materials and structures, classic
numerical studies have identified three main challenges that need to be
addressed [114]. Firstly, the significant memory effects of loading his­
tories require effective extraction of long-term history dependence,
regardless of the loading step sizes. Secondly, ultralong sequences pre­
sent a complexity of O(L2) (L is the sequence length), making them prone
to causing the out-of-GPU-memory problem, which can lead to the
fading of memory effects and gradient explosion or vanishing. Finally,
future information must be masked to prevent data leakage, which re­
quires advanced DL architectures with information transfer capabilities
and parameter sharing mechanisms. ANNs are not suitable for this
branch due to their lack of information transfer and parameter sharing
mechanisms. Instead, more advanced DL architectures such as con­
volutional neural networks (CNNs) and RNNs (as shown in Fig. 8) are
required to address these challenges.
CNNs apply convolutional operations on neighboring elements and
expand an extra dimension (called the channel dimension) to continu­
ously extract local information for memorizing historical states. RNNs
have a natural causal autoregressive property and realize historydependent effects through the sequential transmission of hidden
states. Two main variants of RNNs, LSTM and GRU, integrate different
gates to enhance their long-term memory capabilities. LSTM uses three
gates, namely the input gate, forget gate, and output gate, to selectively
forget or retain information. GRU simplifies LSTM by using two gates,
namely the reset gate and update gate. Compared with LSTM, GRU has
fewer parameters and requires less computation, making it more effi­
cient in many practical applications. In addition to CNNs and RNNs,
attention mechanisms [115] have recently emerged as an effective way
to capture long-term dependencies in dynamic feature studies. Attention
mechanisms use learned weights to selectively focus on certain elements
in the input sequence and dynamically weigh their contributions to the
output (shown in Fig. 9).
Fig. 5. Illustration of the first branch of dynamic feature studies. (“XNN”
represents any neural networks that are adopted for processing the
sequence data).
studies in the second branch can be summarized as follows.
(1) Collect or generate a dataset that contains full-range responses
obtained under different time histories of external stimuli.
(2) Train an AI model with the dataset.
(3) Validate the trained model by comparing its results with the
experimental or elaborate FEA results obtained under new
stimuli.
5.2. AI models in dynamic feature studies
5.3. Typical applications of dynamic feature learning
The AI models used in dynamic feature studies are presented in
Fig. 7, in which the long short-term memory (LSTM) [112] and gated
recurrent unit (GRU) [113] are classified into the recurrent neural
network (RNN) family. Contrary to static feature studies, dynamic
feature studies seldom adopt classic ML algorithms but prefer DL algo­
rithms (this paper classifies ANNs into the domain of DL). This can be
attributed to the sequential nature of dynamic features. Compared to
static features, dynamic features have an additional dimension (i.e.,
sequence length) that can significantly enrich the data and support the
full training of DL models. In addition, the prevalent ML models used in
static feature studies cannot process sequences, making them unsuitable
for dynamic feature learning.
The choice of AI models for the two branches of dynamic feature
studies differs due to the unique characteristics of the data. For the first
Table 2 presents a selection of typical applications of dynamic
feature learning in recent years, with a focus on studies since 2018,
while several early studies are included to exemplify the concept of the
first branch of dynamic feature learning. A more comprehensive list of
applications can be found in Table A1 in the Appendix.
At the construction material level, Jung et al. [116] developed two
parallel ANNs in the total space and deviatoric space respectively to
reproduce rate-dependent constitutive laws. The inputs at each incre­
ment were the outputs derived from the last step. Although simple ANN
models were used, this method was equivalent to the unrolled format of
an RNN model. Wang [118] used LSTM to model the creep evolution of
concrete by learning the data in the early time window, which is a
typical study belonging to the first branch. Bartošák [122] compared the
Fig. 6. Illustration of the second branch of dynamic feature studies.
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responses induced by wind or earthquakes. Jiang and Adeli [135]
developed a wavelet neural network to identify the structural responses
of two high-rising building structures taking into account geometric
nonlinearities. Christiansen et al. [123] investigated the nonlinear re­
sponses of a wind turbine using an ANN in line with the strategy of the
first branch, achieving close agreement with the FEA results. To accel­
erate the Monte Carlo method for assessing the reliability of a frame
structure, Koeppe et al. [126] proposed a surrogate model with LSTM to
predict its time-variant responses, which accurately matched the FE
solutions. Huang et al. [129] trained LSTM and CNN as surrogate models
to predict the nonlinear seismic responses of a two-story, three-span
subway station. The good performance of the surrogate models proved
their ability to capture the evolution characteristics of the probability
density function of layer drift at low computational costs. Xu et al. [132]
developed a robust LSTM model to predict the nonlinear seismic re­
sponses of a building sample, which was able to consider earthquakes
with different spectral characteristics and amplitudes. In particular,
based on the methodology of the second branch, Zhang et al. [127]
complemented a physical loss that considered dynamic equilibrium at
the ground, serving as a regularization term for the training processes
and enhancing the physical interpretability of the computation results.
Fig. 7. Statistical distribution of the intelligent algorithms used in dynamic
feature studies.
6. Composite feature studies
performance of ANN and RNN family models on life prediction for
low-alloy martensitic steel. Wang et al. [114] introduced the Seq2Seq
framework [133] (shown in Fig. 10) and the attention mechanism into
computational analysis in civil engineering. This model reproduced the
nonlinear behavior of BLY160 with strain range dependence effect and
has proven to be more powerful than other sequence DL models in
simulating nonlinear hysteretic responses [134].
Structural components have yet to be studied for dynamic features.
At the structural system level, research has focused on nonlinear
To achieve comprehensive analysis in civil engineering and obtain
the full-range responses of various construction materials and structures,
it is necessary to develop a composite feature learning approach that
incorporates both intrinsic material and structural features as well as
different external stimuli. However, current research on composite
features is limited, as shown in Table 3. These studies adopt a similar
methodology built on conventional member-based macroscopic models.
As reviewed in Section 2, the member-based macroscopic models consist
of a skeleton curve and artificial loading/unloading rules. The key points
on the skeleton curve, such as the yield point and the ultimate capacity
point, are related to the static features of materials or structures.
Therefore, a popular research methodology is using AI models to predict
these key points of a skeleton curve and simulating the full-range re­
sponses of different materials or structures using conventional macro­
scopic models.
As an illustration, Liu et al. [137] proposed the use of an ANN to
predict the key points of a trilinear lumped plasticity (LP) model for RC
columns, which outperformed the LP model that relied on empirical
equations. Han et al. [139] developed an ANN to estimate the skeleton
curve of the Pinch-IMK model for RC beams using structural parameters
such as concrete strength and section geometries as input features.
7. Analysis and discussion
7.1. Quantitative distribution of the current literature
A quantitative analysis is performed on all the collected studies, and
the results are plotted in Figs. 11 and 12. Fig. 11 shows that the numbers
of studies on static feature learning, dynamic feature learning, and
Fig. 8. Illustration of CNN and RNN models used in dynamic feature studies.
(“FC” layer denotes fully-connected layer).
Fig. 9. Illustration of the attention mechanism.
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Table 2
Typical applications of dynamic feature learning.
Construction material level
Structural system level
Author
Year
Task
Algorithm
Jung et al. [116]
Ghavamian et al. [117]
Wang et al. [118]
Gorji et al. [119]
Wang et al. [114]
Feng et al. [120]
Wang et al. [121]
Bartošák [122]
Christiansen et al. [123]
Lagaros et al. [124]
Kim et al. [125]
Koeppe et al. [126]
Zhang et al. [127]
Oh et al. [128]
Huang et al. [129]
Torky et al. [130]
Xue et al. [131]
Xu et al. [132]
2006
2019
2020
2020
2020
2021
2021
2022
2011
2012
2019
2019
2020
2020
2020
2021
2021
2022
Rate-dependent constitutive model of materials (2nd branch)
Constitutive relationship of Perzyna viscoplasticity (2nd branch)
Creep evolution on concrete (1st branch)
Anisotropic Yld2000–2d model with homogeneous anisotropic hardening (2nd branch)
Elasto-plastic constitutive model of BLY160 (2nd branch)
Elasto-plastic constitutive model of high-strength steel (2nd branch)
Constitutive model of concrete and steel (2nd branch)
Lifetime under low-cycle fatigue and thermo-mechanical fatigue loading
Nonlinear dynamic responses of a wind turbine (1st branch)
Nonlinear seismic responses of a frame structure (1st branch)
Nonlinear hysteretic responses of a frame structure (2nd branch)
Elasto-plastic responses of a steel frame structure (2nd branch)
Seismic responses of structural systems (2nd branch)
Seismic responses prediction for RC frame structures (2nd branch)
Seismic responses of a subway station (1st branch)
Nonlinear seismic responses of structural buildings (2nd branch)
Wind-induced structural responses in a transmission tower (2nd branch)
Nonlinear structural seismic responses of a frame structure (2nd branch)
ANN
LSTM
LSTM
ANN, GRU
Seq2Seq, attention
LSTM
CNN
ANN, LSTM, GRU
ANN
ANN
CNN
LSTM
CNN
CNN
CNN, LSTM
CNN, LSTM, ANN
LSTM
LSTM
composite feature learning decrease in the listed order, indicating a rise
in research barriers. The methodology of static feature studies is
straightforward and similar to that of ordinary ML problems. In contrast,
dynamic feature studies must consider the distinctive characteristics of
computational analysis in civil engineering, such as the significant
memory effect and causal autoregression. The composite feature studies,
which are deemed most difficult, do not yet have fully-intellectualized
solutions and still rely on conventional artificial constitutive rules. The
increasing difficulty levels of these three research categories also man­
ifest in the complexity of commonly used AI algorithms. Classic machine
learning algorithms are typically adopted in static feature studies, which
are relatively simple and have been effectively integrated by commercial
packages. Dynamic feature studies, on the other hand, require more
complex deep learning models and advanced algorithms to address
numerical challenges, which necessitates the design of proper archi­
tectures and tuning of hyperparameters. Fig. 12 presents the quantita­
tive distribution of the typical applications of each research category at
different levels. The number of studies that concentrate on the structural
member level using static feature learning ranks the first, indicating that
researchers are more inclined to directly calculate static mechanical
properties such as strength and stiffness, which also coincides with en­
gineering design habits.
fitting, leading to accelerated discovery of computing methods for
complex structures and novel materials. Dynamic feature studies focus
on predicting full-range mechanical responses of the material or struc­
ture sample of interest under random external stimuli, taking into ac­
count material and geometric nonlinearities, especially the historydependent effect induced by cyclic loading. In these studies, AI models
serve as surrogate models, accelerating the simulation processes instead
of using conventional constitutive formulations and numerical solu­
tions. This approach achieves acceptable accuracy under certain con­
ditions with a low computational cost. Composite feature studies
synthesize the former two research categories to obtain full-range re­
sponses of different construction materials or structures.
AI models establish quantitative relationships between input features
and target responses, replacing traditional numerical methods and
eliminating the need for manual intervention, while achieving higher
accuracy and efficiency. Additionally, the introduction of AI techniques
reduces the domain knowledge requirements, as AI models automati­
cally recognize the underlying patterns in the input data, accelerating
the discovery of mechanical laws from a phenomenological perspective.
However, despite the progress made, limitations exist that impede
further application of research achievements.
Firstly, current research primarily focuses on upgrading AI models
rather than addressing data-related issues. It has been widely acknowl­
edged that input data plays a crucial role in determining the final per­
formance of AI models [19]. However, many studies still relies on the
manual selection of input features, and few have reported how to
normalize inputs with different physical units and value magnitudes.
Manual intervention in the feature selection process results in inevitable
information loss, especially for problems that are not well explained by
classic theories, such as the shear behavior of concrete structures This
limitation hinders AI models from surpassing human knowledge to
discover new scientific laws. Therefore, to fully exploit the "end-to-end"
7.2. Discussion of current progress
The previous sections have demonstrated the effectiveness of AI
techniques in facilitating computational analysis of construction mate­
rials and structures in civil engineering. Static feature studies aim to
establish mapping relationships between intrinsic structural or material
parameters, such as section geometries and material mixture pro­
portions, and their mechanical performance, such as strengths and
failure modes. In these studies, AI models replace traditional function
Fig. 10. Illustration of Seq2Seq architecture. (“XNN” and “YNN” represent any embedded neural networks used to process the sequences).
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these models is limited by human knowledge in designing appropriate
hysteretic rules, which prevents them from fully leveraging the excep­
tional regression capability of AI. In summary, composite feature studies
that are completely based on AI solutions are still lacking.
Last but not least, the prevailing models in static feature studies and
dynamic feature studies, though both fall within the scope of AI, lack
coordination, impeding the development of composite feature learning.
As discussed in Sections 4 and 5, static feature studies tend to rely on ML
models, while dynamic feature studies favor DL models. Classical ML
models, such as SVMs and DTs, estimate target indicators directly but
are unable to learn representations. Unlike DL models, ML models
cannot extract useful information, such as hidden states, from the in­
termediate computing processes, making it difficult to incorporate static
feature information into dynamic feature learning. Furthermore, DL
models are optimized within backpropagation algorithm frameworks
that use computational graphs [28,29], which makes it difficult to
integrate ML models and implement end-to-end joint training.
Table 3
Composite feature studies.
Structural
member
level
Structural
system
level
Author
Year
Task
Algorithm
Luo et al. [136]
2018
SVM
Liu et al. [137]
2019
Liu et al. [138]
2021
Han et al. [139]
2021
Zarringol et al.
[140]
2022
Wen et al. [141]
2022
SoleimaniBabakamali et al.
[142]
2022
Hysteretic behavior of
RC columns
Pseudo-static cyclic
behavior of RC
columns
Hysteretic behavior of
RC columns
Hysteretic response of
RC beams using the
Pinch-IMK model
Load-shortening
curves of CFST
columns
Floor seismic
responses of regular
frame structures
Probabilistic seismic
demand analysis of RC
frames
ANN
SVM
ANN
ANN
CNN
LSTM,
CNN
8. An example of intelligent composite feature learning
To demonstrate the exceptional performance of AI models compared
with conventional numerical methods and provide an example of com­
posite feature learning as well, this section introduces a recent study by
the authors [143] on an end-to-end DL framework – named Deep
Structural Nonlinear Analysis (DeepSNA) – for computational analysis in
civil engineering, which covered both the data and model sides and was
completely based on DL.
prediction capabilities of AI models, it is essential to establish a general
data organization template and preprocessing method.
Secondly, most static feature studies are interested in mechanical
properties such as material strengths, structural loading capacities, and
maximal interstory drift ratios. However, these mechanical properties
are closely related to loading paths, which can experience strengthening
or softening induced by cyclic loads [55,56], thereby affecting the re­
sults. Therefore, most static feature studies can be seen as simplified
models that neglect the influence of loading path dependence. On the
other hand, current state-of-the-art dynamic feature learning studies can
only simulate the full-range responses of a specific sample, with input
features limited to external stimuli. Although these AI models are
generalizable and can be adapted to different scenarios, such as
extending from the material level to the structural level by substituting
the <strain, stress> pair with the <displacement, load> pair without
altering the model architecture, they are unable to generalize to other
construction materials or structures and require retraining from scratch.
In short, to accurately predict the mechanical behavior of various ma­
terials and structures, core models must consider both intrinsic prop­
erties and external stimuli.
Third, the composite feature studies presented in Section 6 have
succeeded in obtaining the full-range responses of various structures.
However, their research ideas are still essentially rooted in static feature
learning. Furthermore, according to Cannikin’s law, the performance of
8.1. Data interface
To maximally preserve raw information and minimize the human
intervention involved with the input features, a general data interface
schema with a core concept of “feature modules” (FMs) was designed by
drawing from the “assembly” philosophy of the classic FE method. This
concept breaks down the constituent elements of construction materials
or structures into multiple modules and further classifies them into two
categories in terms of their repeatability: fixed-length feature modules
(FLFMs) and variable-length feature modules (VLFMs), which are orga­
nized as vectors and sequences, respectively. Take a steel plate shear
wall (SPSW) structure as an example. The stiffeners of an SPSW can be
arranged as a VLFM because multiple stiffeners commonly exist to
prevent buckling. Within the stiffener FM, feature fields such as
Fig. 12. Distribution of the applications in different scenarios. (SF: static
feature studies; DF: dynamic feature studies; CF: composite feature studies).
Fig. 11. Distribution of the three research categories.
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Table 4
Two feature modules used for SPSW structures [143].
Feature
field
Data
type
Feature
type
Description
Infill panels: VLFM
n
INT
b
FLOAT
h
FLOAT
t
FLOAT
E
FLOAT
fy
FLOAT
eu
FLOAT
FLOAT
fu
cb
FLOAT
DENSE
DENSE
DENSE
DENSE
DENSE
DENSE
DENSE
DENSE
DENSE
cc
DENSE
The floor number.
The width of the infill panel on the given floor (mm).
The height of the infill panel on the given floor (mm).
The thickness of the infill panel on the given floor (mm).
The elastic modulus of the infill panel on the given floor (GPa).
The yield strength of the infill panel on the given floor (MPa).
The limit strain of the infill panel on the given floor (με).
The tensile strength of the infill panel on the given floor (MPa).
The connections between the infill panel and the frame beams, which are measured by the bolt spacing (mm). A value of b indicates that the
panel does not connect with the beams, while a value of 0 indicates that the panel is welded to the beams.
The connections between the infill panel and the frame columns, which are measured by the bolt spacing (mm). A value of h indicates that
the panel does not connect with the columns, while a value of 0 indicates that the panel is welded to the columns.
FLOAT
Perforations: VLFM
n
INT
s
INT
cx
FLOAT
cy
FLOAT
p1
FLOAT
p2
FLOAT
DENSE
SPARSE
DENSE
DENSE
DENSE
DENSE
The floor number.
The shape of the perforation, where 0 and 1 indicate elliptic and rectangular openings, respectively.
The distance between the center of the perforation and the bottom-left point of the panel along the x-axis (mm).
The distance between the center of the perforation and the bottom-left point of the panel along the y-axis (mm).
The first geometric parameter of the perforation (mm). For an elliptic opening, p1 is the diameter along the x-axis; for a rectangular
opening, p1 is the width along the x-axis.
The second geometric parameter of the perforation (mm). For an elliptic opening, p2 is the diameter along the y-axis; for a rectangular
opening, p2 is the height along the y-axis.
positions and section stiffnesses are configured to describe each stiff­
ener. On the other hand, since only one top beam is contained in an
SPSW structure, the top beam feature module was an FLFM. Table 4
gives a concrete example of two feature modules used for SPSW
structures.
Aggregating the sequences in VLFMs. To realize this goal, a pre-attention
layer was developed with the standard attention mechanism followed by
average pooling, which not only uncovered the collaborative effect
within the VLFM but also introduced interactions between different
constituent elements. (2) Learning the coupling relationships between
different FMs. Due to material and geometric nonlinearity, the contri­
butions of various FMs to the overall responses did not satisfy the
principle of superposition. Accordingly, a DCN [144] was introduced,
which consisted of a cross network tower and a deep network tower. The
cross network tower was responsible for memorability (i.e., similar
materials or structures should yield similar responses) and only took a
complexity of O(d) (d was the total dimensionality after the
pre-attention layer) to increase the order of representation, effectively
preventing the combinatorial explosion problem in cross learning. The
deep network tower was a feedforward neural network (FFN) that
8.2. Static feature learning
In contrast to directly predicting mechanical indicators, the static
feature learning part of DeepSNA introduced the idea of representation
learning to process the input data into a vector containing the dense
information of given static features, which was analogous to the concept
of embedding. Specifically, a DL model named Pre-Attention Deep &
Cross Network (PADCN) was proposed, as shown in Fig. 13(a).
The PADCN model was intended to implement two functions. (1)
Fig. 13. Illustration of DeepSNA [143].
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Journal of Industrial Information Integration 33 (2023) 100470
pursued generalization through DL.
The representation vector learned by the PADCN model was then
passed into the downstream dynamic feature model to predict the fullrange mechanical responses of different structures.
8.5. Summary of AI modeling
To better clarify the general methodology of AI-based computational
analysis in civil engineering, the processes of creating and validating AI
models can be summarized as follows:
Data collection and preprocessing: Collect relevant data from various
sources and preprocess it to remove noise and outliers and transform it
into a suitable format for modeling. In our scenarios, static feature
learning typically uses fixed-size vectors and structured tables, while
dynamic feature learning should deal with sequences of variable
lengths.
8.3. Dynamic feature learning
The dynamic feature model in DeepSNA – named Mechanical
Transformer (Mechformer) – was designed in line with the three char­
acteristics of the second branch described in Section 5.2. Based on the
reference [114], the Seq2Seq framework with the attention mechanism
was upgraded to the Transformer architecture [115], which featured
better parallelization, as shown in Fig. 13(b). To address the large
memory cost brought about by the standard attention mechanism in
long-sequence cases, the Fast Attention via Positive Orthogonal Random
Features (FAVOR+) algorithm [145] was utilized instead, reducing the
space and time complexity from O(L2) to O(L). In the decoder, a GRU
was adopted to naturally realize the causal generation of target re­
sponses. The global memory information was extracted by the encoder
and concatenated with the outputs derived from the first deep GRU
layer. In this manner, the history dependence effect was augmented,
alleviating the memory fading problem of GRU in long-sequence cases.
By aggregating the PADCN and Mechformer, DeepSNA could
implement joint training for both models in an end-to-end manner.
Therefore, DeepSNA realized an entire pipeline from the raw data inputs
to full-range mechanical response predictions for arbitrary materials or
structures, forming a computational framework in civil engineering.
(1) Feature selection and engineering: Select relevant features from
the preprocessed data and engineer new features to improve the
model’s performance.
(2) Model selection and training: Select a suitable machine learning
or deep learning algorithm and train the model on the pre­
processed and engineered data. For pure static feature studies,
classic machine learning and deep learning algorithms such as
ANN, SVM, and boosting are commonly used. For static feature
representation learning or dynamic feature prediction, complex
deep learning models such as RNN and CNN are often preferred to
capture the temporal dependencies and non-linear relationships.
(3) Hyperparameter tuning: Once the model is trained, tune its
hyperparameters such as the number of layers in neural networks
and the learning rate set in the optimizer to optimize its perfor­
mance on a validation set.
(4) Model evaluation: Evaluate the model’s performance on a sepa­
rate test set to determine its accuracy, precision, recall, or other
metrics according to the problem. For most mechanical response
prediction tasks, regression loss functions such as the mean ab­
solute error (MAE, e.g., L1 loss) and mean squared error (MSE, e.
g., L2 loss) are commonly used to directly assess the performance
of the model on the evaluation set and test set.
(5) Deployment and monitoring: Once the model is validated, deploy
it in a production environment and monitor its performance over
time to ensure that it continues to perform well.
8.4. Performance of DeepSNA
To demonstrate the performance of DeepSNA in comparison with
conventional numerical methods, a numerical experiment based on
SPSW structures was performed. The data were collected from historical
literature and FE models and further extended by data augmentation
algorithms [143]. The Adam optimizer [146] was used with a tri-step
learning rate schedule.
Fig. 14 presents the results obtained on the test dataset [147–149].
DeepSNA reproduced the highly nonlinear hysteretic responses of
different SPSW structures, capturing the cyclic hardening induced by the
strain range dependence effect, the breathing effect induced by the cy­
clic loading/unloading of shear bands, and the strength and stiffness
degradation induced by the damage under large deformations. It was
noteworthy that the training set contained stiffened SPSWs and perfo­
rated SPSWs but did not have specimens with the combination of these
two constructions. However, as shown in Fig. 14(c), DeepSNA general­
ized to predict the response of specimen SPSW-1, which was a stiffened
perforated SPSW (illustrated in Fig. 15). Therefore, DeepSNA was able to
discover the underlying coupling mechanisms between different con­
struction details, exhibiting reasonable generalization capability. Fig. 14
also shows the comparison of the performance of DeepSNA and the
conventional FE method. DeepSNA acquired more accurate results than
FEA, especially for specimens that experienced strong geometric
nonlinearity and strength softening. Moreover, in contrast with the
conventional FE method that took hours to simulate only one specimen,
DeepSNA predicted the responses of 16 specimens (i.e., the batch size of
the test set) in less than 10 s without formal deployment, achieving a
computational efficiency enhancement of at least 1000 times.
Therefore, DeepSNA was capable of predicting the full-range re­
sponses of different materials or structures and was a concrete example
of composite feature learning. This example also demonstrated the
exceptional performance of AI models compared with that of conven­
tional FE methods, exhibiting far greater computational efficiency with
high accuracy.
These steps are typically repeated multiple times with different
variations of the data, features, models, and hyperparameters to find the
best-performing model or the model that achieves the engineering
requirements.
9. Open problems
As demonstrated by the reviews, AI-based computational analysis
techniques have made significant progress in many explorative sce­
narios in civil engineering. However, it is important to recognize that
there is still a considerable gap between current achievements and their
practical application in engineering. In order to help bridge this gap, this
section highlights four open problems from a practical perspective that
need to be addressed to facilitate the implementation of academic
research in engineering applications.
9.1. Few-shot and incomplete-shot learning
The foundation of AI technology lies in big data. However, in civil
engineering, due to historical and realistic factors, the data obtained
from vast experimental studies and site investigations lack a systematic
collection, resulting in a deficiency of benchmark datasets. Furthermore,
due to the diversity and updating of materials and structures, it is
challenging for a dataset to cover all parameters. Therefore, developing
few-shot and incomplete-shot learning methods in civil engineering is
essential. These methods are instrumental in exploiting the full power of
large-scale intelligent models from a limited amount of data and
enhancing their generalization capabilities.
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Journal of Industrial Information Integration 33 (2023) 100470
Fig. 14. Comparison between DeepSNA and FEA [143].
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Journal of Industrial Information Integration 33 (2023) 100470
Fig. 15. The generalization of DeepSNA [143].
achieve high performance on a range of downstream tasks. This "pre­
training and fine-tuning" approach has been successful in computer
vision and natural language processing. However, developing largescale pretrained models for civil engineering faces significant chal­
lenges, such as designing pretrained objectives, learning latent knowl­
edge across structures with different mechanical behaviors, and
collecting appropriate unlabeled data. Despite these challenges, large
pretrained models hold considerable promise for civil engineering ap­
plications, as they could reduce the need for costly experiments and
speed up the development of new engineering solutions.
9.2. High-fidelity representation of complex structural systems
The analysis of structural systems is the focal point of engineering
applications. While the studies reviewed in this paper have made
considerable progress in analyzing regular structural systems such as
frame structures, which are relatively easy to digitalize using simplified
data structures, the representation of more complex structural systems
remains a key challenge. Unlike construction materials and individual
structural members, structural systems exhibit a high degree of topo­
logical diversity [150] and include a variety of member configurations
that must be considered in the analysis. The complete description of a
structural system cannot be organized into ordinary linear data struc­
tures, such as vectors, grids, or sequences. Therefore, developing
high-fidelity representations of complex structural systems is crucial for
enabling accurate analysis and prediction using AI models and
advancing their practical application in engineering.
10. Conclusions
Over the past few years, new-generation AI technologies, repre­
sented by ML and DL, have emerged as promising tools for the compu­
tational analysis of materials and structures in civil engineering. This
review paper aims to assess the current progress in this emerging field,
highlighting both state-of-the-art achievements and future challenges.
The main conclusions can be summarized as follows.
9.3. Physical interpretability
Civil engineering is based on physical principles, and traditional
numerical methods strictly adhere to mechanical laws and equations,
making their computational results easy to understand and interpret
[151]. In contrast, many AI models, especially deep neural networks, are
often criticized for their black-box nature, with complex and opaque
internal workings that make it difficult to understand how they arrive at
their predictions. In engineering applications, where safety is of utmost
importance, this lack of interpretability is unacceptable. Therefore,
exploring physically interpretable AI models is indispensable in civil
engineering to ensure the theoretical correctness of computational re­
sults. Such models would enable engineers to understand how the AI
yields its predictions and make necessary corrections or modifications to
improve safety and reliability.
(1) The introduction of AI in computational analysis of civil engi­
neering is motivated by the fact that most traditional computa­
tional models rely on phenomenological regression, which can be
replaced by AI algorithms to improve their expressiveness.
(2) AI-based computational analysis in civil engineering can be
classified into three major directions based on input features:
static feature studies, dynamic feature studies, and composite
feature studies. The difficulty and complexity of the popular al­
gorithms used in these categories increase in turn, while the
number of studies decreases.
(3) Static feature studies use AI models to predict scalar mechanical
properties based on intrinsic material or structural parameters.
Dynamic feature studies aim to predict future responses through
past responses or simulate full-range responses through sequence
learning. Composite feature studies use AI models to predict the
key points of artificial constitutive models, which are then used to
simulate the full-range responses of different materials or
structures.
(4) Current progress is limited by insufficient consideration of the
data side and the lack of end-to-end composite feature learning.
An example is highlighted to address these limitations and to
demonstrate the exceptional performance of AI models in terms
9.4. Large pretrained models in civil engineering
Most current AI models in civil engineering are task-specific,
requiring a large amount of labeled data to be trained from scratch.
This data is typically obtained through costly experiments or finite
element analysis, which involves a significant amount of manual work.
In contrast, large pretrained models leverage unsupervised or selfsupervised learning paradigms that require only unlabeled data. These
models can be quickly fine-tuned with a small amount of labeled data to
14
C. Wang et al.
Journal of Industrial Information Integration 33 (2023) 100470
Table A1
Literature list for the review.
App. level
Static feature studies
Construction material
level
Structural member
level
Structural member
level
Structural member
level
Ref.
Year
Prediction target
AI algorithms
[106]
[152]
[153]
[154]
[155]
[156]
[107]
[108]
[157]
[158]
[159]
[160]
[161]
[162]
[163]
[72]
[73]
[74]
[75]
[76]
[77]
[78]
[79]
[81]
[82]
[83]
[164]
[165]
[166]
[80]
[167]
[168]
[169]
[170]
[171]
[172]
[173]
[174]
[175]
[176]
[84]
[85]
1997
2008
2009
2010
2011
2012
2013
2013
2013
2013
2013
2014
2015
2017
2017
2018
2018
2018
2019
2019
2020
2020
2021
2021
2021
2021
2021
2021
2021
2021
2022
2006
2010
2012
2013
2014
2014
2014
2016
2017
2018
2018
Compressive strength of concrete
Elastic modulus of normal/high-performance concrete (HPC)
Steel-concrete bond strength
Compressive strength of no-slump concrete
Uniaxial compressive strength of jet grouting materials
Bond strength of spliced steel bars in concrete
Compressive strength of HPC
Splitting tensile strength of concrete
Elastic modulus of recycled aggregate concrete
Rapid chloride permeability of self-consolidating concrete
Drying shrinkage of concrete
Compressive strength of HPC
Bond strength of GFRP bars in concrete
Compressive strength of NC and HPC
Compressive strength of concrete
Compressive strength of silica fume concrete
Strength of concrete containing agricultural and construction wastes
Compressive and tensile strength of HPC
Dynamic increase factor for SFRC
Mechanical properties of composites
Compressive strength of concrete
Compressive strength of HPC
Bond strength of near-surface-mounted FRP bonded to concrete
Compressive strength of rubber concrete
CFRP-steel bond strength
Compressive and flexural strength of SFRC
Post-cracking tensile strength of fiber-reinforced concrete
Ultimate strength of FRP-confined concrete
Compressive and tensile strength of HPC
Compressive strength of recycled aggregate concrete
Mechanical properties of composite laminate
Shear strength of SFRC beams
Compressive strength of FRP-confined concrete columns
Buckling and post-buckling loads of compression members
Failure mode, shear strength and deformation capacity of infilled walls
Compressive strength and strain of FRP-confined columns
Shear strength of FRP-reinforced concrete flexural members without stirrups
Shear strength of RC beam-column joints
Punching shear capacity of FRP-reinforced concrete slabs
Compressive strength of FRP-confined concrete circular columns
Shear resistance of FRP bars-reinforced concrete beams
Failure mode and shear strength of RC beam-column joints
[177]
[178]
[179]
[86]
[180]
[87]
[181]
[88]
[105]
[182]
[99]
[183]
[184]
[89]
[185]
[90]
[186]
[187]
[188]
[91]
[189]
[92]
[93]
[190]
[191]
2018
2018
2019
2019
2019
2019
2019
2019
2019
2019
2019
2020
2020
2020
2020
2020
2020
2020
2020
2021
2021
2021
2021
2021
2021
Shear strength of SFRC beams
Shear strength of squat RC shear walls
Punching shear capacity of SFRC slabs
Load-carrying capacity and mode failure of beam-column joint
Failure mode of circular RC bridge columns
Thermal and structural response of RC members
Axial compression capacity of SCFST columns
Shear strength of steel fiber-unconfined RC beams
Shear strength of SFRC beams
Failure modes of ductile and non-ductile concrete joints
In-plane failure modes of infilled RC walls
Seismic failure mode identification of RC shear walls
Failure mode and bearing capacity of RC columns
Shear capacity of one-way slabs under concentrated loads
Failure mode and shear capacity of UHPC beams
Shear capacity of deep RC beams
Shear strength of internal RC beam-column joints
Shear strength of SFRCB without stirrups
Axial compression capacity of circular CFST with UHPC
Plastic hinge length of RC columns
Punching shear strength of flat RC slabs without transverse reinforcement
Buckling load of fiber-steering composite cylinders
Strength of CFST columns under concentric loading
Plastic hinge of rectangular RC columns
Failure mode of beam-column joints
[192]
[193]
[194]
[195]
2021
2021
2021
2021
Loading capacity of RC shear walls
Shear strength of SFRC beams
Shear strength of RC deep beams with/without web reinforcements
Shear capacity of slender RC structures with steel fibers
ANN
ANN
ANN
ANN
SVM
ANN
ANN, boosting, bagging
SVM
ANN
ANN, LNR
ANN
ANN, SVM, DT, LR, bagging
ANN
DT
Deep restricted Boltzmann machine
ANN
ANN
ANN
RF
CNN
boosting
Genetic programming, boosting
bagging
ANN
ANN, SVM, DT, boosting, bagging
KNN, LNR, SVM, ANN, boosting
ANN
SVM
SVM, ANN, boosting
ANN, SVM
ANN
ANN
ANN
ANN
ANN
ANN
ANN
LNR, symbolic regression
SVM
ANN
ANN
LR, LNR, KNN, Naïve Bayes, SVM, DT,
bagging
SVM
ANN
LNR, ANN
Extreme learning machine
KNN, DT, Naïve Bayes, ANN, bagging
ANN
ANN
SVM
multi-expression programming
DT
DT, LR, ANN, RF, SVM, boosting
Naïve Bayes, KNN, DT, bagging, boosting
boosting
ANN
SVM, ANN, genetic programming, KNN
SVM, ANN, boosting
boosting
SVM
ANN
boosting
LNR, SVM, DT, KNN, bagging, boosting
DT, LNR, ANN, bagging
boosting
SVM, bagging, boosting
KNN, LR, SVM, ANN, Naïve Bayes, DT,
bagging, boosting
ANN
LNR, DT, SVM, KNN, ANN, bagging, boosting
bagging, boosting
bagging, Gaussian process regression
(continued on next page)
15
C. Wang et al.
Journal of Industrial Information Integration 33 (2023) 100470
Table A1 (continued )
App. level
Structural system level
Structural system
level
Ref.
Year
Prediction target
AI algorithms
[196]
[197]
2021
2021
Load-carrying capacity of FRP-RC columns
Failure mode of steel column base plate connection
[198]
2021
Structural performance under fire
[199]
[94]
[192]
[200]
[201]
[202]
[203]
[204]
[205]
[206]
[95]
[96]
[207]
2021
2021
2021
2021
2021
2021
2021
2021
2021
2021
2022
2022
2022
Shear capacity of squat flanged RC walls
Ultimate strength of CFCFST
Lateral loads of RC shear walls
Axial compressive capacity of CCFST columns
Failure mode of RC columns
Shear strength of squat RC walls
Shear strength of RC shear walls
Load capacity of shear walls
Shear capacity of FRP reinforced concrete members
Axial load capacity of rectangular CFST columns
Axial compression of rectangular CFST columns
Shear strength of FRP reinforced concrete members
Seismic failure mode of RC shear walls
[208]
2022
Failure modes, strength, and deformation capacity of RC shear walls
[209]
[210]
[97]
[211]
[98]
[212]
[213]
2009
2010
2017
2018
2019
2019
2020
[100]
[101]
[102]
[103]
[214]
[215]
[216]
[217]
[218]
[219]
[220]
[221]
2021
2021
2021
2021
2021
2021
2022
2022
2022
2022
2022
2022
Damage indices of 2D RC frames
Global drift capacities of RC frames
Maximum interstorey drift ratio of RC frames
Maximum interstorey drift ratio of RC frames
Peak story drift ratios of RC frame buildings
Maximum interstorey drift ratio of RC frame buildings
Maximum interstory drift ratio and maximum displacement of a planar RC building
structure under earthquakes
Maximum story drift and collapse status of RC frame buildings
Seismic drift demands of steel special moment resisting frames
Non-linear buckling load of imperfect reticulated shells
Maximum drift of RC frames
Fundamental time period of masonry infilled RC frames
Seismic drift responses of planar steel moment frames
Limit state index of existing RC structures
A multidimensional limit state function of RC buildings
Shear force, bending moment and section curvature ductility of tall pier bridges
Two damage indices of RC frames
Collapse fragility curve of steel moment-frame buildings
Drift, velocity and acceleration of RC frame buildings with soft/weak story
boosting
SVM, Naïve Bayes, KNN, DT, bagging,
boosting
DT, bagging, boosting, Deep residual neural
network
ANN
SVM, ANN
ANN
boosting
ANN, DT
boosting
ANN
SVR
SVR
ANN
ANN, Gaussian process regression
ANN
Naïve Bayes, KNN, LR, SVM, DT, ANN,
bagging, boosting
LR, NB, SVM, DT, KNN, ANN, boosting,
bagging
ANN
ANN
ANN
ANN
SVM
ANN
ANN
Naïve Bayes, KNN, DT, bagging, boosting
bagging
SVM
LNR, SVM, KNN, bagging, boosting
ANN, LNR, SVM, KNN, DT, bagging, boosting
ANN, boosting
ANN
ANN
ANN, LSTM
LNR, DT, boosting, KNN, ANN
boosting
ANN
2006
2007
2019
2019
2020
2020
2021
2021
2022
2022
2022
2022
2005
2007
2011
2012
2019
2019
2019
2019
2020
2020
2020
2021
2021
2021
2021
2021
2021
2022
2022
2022
2022
Time-dependent behavior of concrete
Multi-axial constitutive models for FRP composites
Constitutive model for Perzyna viscoplasticity
Plasticity constitutive laws for aluminum alloy
Anisotropic Yld2000–2d model
Constitutive model for BLY160 steel
Constitutive laws of fiber-reinforced composites
Elastoplastic behavior for J2-plasticity
Constitutive models for concrete and steel
Elasto-plastic deformation behavior of 3D-foam structures
Constitutive laws of composites
Lifetime under low-cycle fatigue and thermo-mechanical fatigue loading
Earthquake responses of high-rise building structures
Dynamic response of slender marine structures
Dynamic response of a simplified wind turbine
Seismic response of a 2-story RC building
Nonlinear time analyses of a single-DOF structure
Nonlinear structural response modeling of specific structures
Elasto-plastic response of a structure
Wind-induced responses of a tall building
Seismic response of sample structures
Seismic displacement responses of a RC building
Seismic response of a 3-story moment resisting frame
Nonlinear seismic responses of a subway station
Seismic responses of a building
Nonlinear response of an engineering structure
Wind-induced dynamic response of a transmission tower-line system
Dynamic strain of a structure under stimuli
Seismic responses of a building
Nonlinear seismic response of a frame structure
Structural response under seismic excitations
Hysteretic response of a brace structure under different loading protocols
Damage states with regards to different ground motion time histories
ANN
ANN
LSTM
GRU
GRU
Seq2Seq, attention mechanism
ANN
ANN
Temporal CNN
ANN
ANN
ANN, LSTM, GRU
ANN
ANN
ANN
ANN
CNN
LSTM
LSTM
CNN
CNN
CNN
LSTM
LSTM, CNN
LSTM, CNN
ANN, Monte Carlo
LSTM
CNN
CNN
LSTM
Seq2Seq, attention mechanism
Transformer
LSTM, CNN
Dynamic feature studies
Construction material
[116]
level
[222]
[117]
[223]
[119]
[114]
[224]
[225]
[121]
[226]
[227]
[122]
Structural System level
[135]
[228]
[123]
[124]
[125]
[229]
[126]
[230]
[127]
[128]
[231]
[129]
[130]
[232]
[233]
[234]
[235]
[132]
[236]
[237]
[238]
(continued on next page)
16
C. Wang et al.
Journal of Industrial Information Integration 33 (2023) 100470
Table A1 (continued )
Year
Prediction target
AI algorithms
Composite feature studies
Structural member
[239]
level
[136]
[137]
[138]
[140]
[240]
[143]
App. level
2008
2018
2019
2021
2022
2022
2022
Crushing behavior of axially loaded elliptical composite tubes
Response backbone curve model of RC columns subjected to cyclic loading
A lumped plasticity model of RC columns
Skeleton curve prediction for a cyclic model of RC columns
Key points of the axial load-shortening curve of CFST columns
Lateral cyclic response of post-tensioned base rocking steel bridge piers
Full-range responses of different materials or structures
[241]
[141]
[142]
2022
2022
2022
Encode the Bouc-Wen model to simulate S-shaped steel dampers
Floor seismic responses of regular frame structures
Probabilistic seismic demand analysis of RC frames
ANN
SVM
ANN
SVM
ANN
LNR, KNN, SVM, bagging, boosting
Transformer, Deep & Cross Network,
attention mechanism
LSTM
CNN
LSTM, CNN
Structural system level
Ref.
of accuracy and efficiency compared with conventional numeri­
cal methods.
(5) From an engineering applications perspective, four open prob­
lems that represent major challenges and future research di­
rections are suggested: few-shot and incomplete-shot learning,
high-fidelity representations of structural systems, physical
interpretability, and large-scale pretrained models.
[5] M.X. Tao, J.G. Nie, Fiber beam-column model considering slab spatial composite
effect for nonlinear analysis of composite frame systems, J. Struct. Eng. 140 (1)
(2014), 04013039, https://doi.org/10.1061/(ASCE)ST.1943-541X. 0000815.
[6] C. Wang, J.S. Fan, L.Y. Xu, X. Nie, Cyclic hardening and softening behavior of the
low yield point steel: implementation and validation, Eng. Struct. 210 (2020),
110220, https://doi.org/10.1016/j.engstruct.2020.110220.
[7] Z.Y. Zhang, R. Ding, J.S. Fan, M.X. Tao, X. Nie, Numerical study of reinforced
concrete coupled shear walls based on a two-dimensional finite element model,
Eng. Struct. 244 (2021), 112792, https://doi.org/10.1016/j.
engstruct.2021.112792.
[8] J.C. Simo, T.J.R. Hughes, Computational Inelasticity, Springer Science & Business
Media, 2006. ISBN: 0387975209.
[9] T. Belytschko, W.K. Liu, B. Moran, K. Elkhodary, Nonlinear Finite Elements For
Continua and Structures, John Wiley & Sons, 2013. ISBN: 9781118632703.
[10] A.A. Sousa, Y. Suzuki, D. Lignos, Consistency in solving the inverse problem of the
Voce-Chaboche constitutive model for plastic straining, J. Eng. Mech. 146 (9)
(2020), 04020097, https://doi.org/10.1061/(ASCE)EM.1943-7889.0001839.
[11] A.R. Hartloper, A.A. SousaA, D. Lignos, Constitutive modeling of structural steels:
nonlinear isotropic/kinematic hardening material model and its calibration,
J. Struct. Eng. 147 (4) (2021), 04021031, https://doi.org/10.1061/(ASCE)
ST.1943-541X.0002964.
[12] X.Z. Lu, L.L. Xie, H. Guan, Y.L. Huang, X. Lu, A shear wall element for nonlinear
seismic analysis of super-tall buildings using OpenSees, Finite Elem. Anal. Des. 98
(2015) 14–25, https://doi.org/10.1016/j.finel.2015.01.006.
[13] J. Li, Z.A. Wang, F. Li, B. Mou, T. Wang, Experimental and numerical study on the
seismic performance of an L-shaped double-steel plate composite shear wall,
J. Build. Eng. (2022), 104015, https://doi.org/10.1016/j.jobe.2022.104015.
[14] Z.G. Mu, Y.Q. Yang, Experimental and numerical study on seismic behavior of
obliquely stiffened steel plate shear walls with openings, Thin Walled Struct. 146
(2020), 106457, https://doi.org/10.1016/j.tws.2019.106457.
[15] M. Brocca, L.C. Brinson, Z.P. Bažant, Three-dimensional constitutive model for
shape memory alloys based on microplane model, J. Mech. Phys. Solids 50 (5)
(2002) 1051–1077, https://doi.org/10.1016/S0022-5096(01)00112-0.
[16] L.Y. Xu, M.X. Tao, X. Nie, J.S. Fan, Modeling techniques for strain-rangedependent hardening behavior of low-yield-point steel shear panel dampers,
J. Struct. Eng. 143 (12) (2017), 04017172, https://doi.org/10.1061/(ASCE)
ST.1943-541X.0001896.
[17] R. Ding, M.X. Tao, X. Nie, Y.L. Mo, Analytical model for seismic simulation of
reinforced concrete coupled shear walls, Eng. Struct. 168 (2018) 819–837,
https://doi.org/10.1016/j.engstruct.2018.05.003.
[18] Y. LeCun, Y. Bengio, G. Hinton, Deep learning, Nature 521 (7553) (2015)
436–444, https://doi.org/10.1038/nature14539.
[19] I. Goodfellow, Y. Bengio, A. Courville, Deep Learning, MIT press, 2016. ISBN:
9780262035613.
[20] H. Salehi, R. Burgueño, Emerging artificial intelligence methods in structural
engineering, Eng. Struct. 171 (2018) 170–189, https://doi.org/10.1016/j.
engstruct.2018.05.084.
[21] M.L.C. Pena, A. Carballal, N. Rodríguez-Fernández, Artificial intelligence applied
to conceptual design. A review of its use in architecture, Autom. Constr. 124
(2021), 103550, https://doi.org/10.1016/j.autcon.2021.103550.
[22] K. Hornik, M. Stinchcombe, H. White, Multilayer feedforward networks are
universal approximators, Neural Netw. 2 (5) (1989) 359–366, https://doi.org/
10.1016/0893-6080(89)90020-8.
[23] K. Hornik, M. Stinchcombe, H. White, Universal approximation of an unknown
mapping and its derivatives using multilayer feedforward networks, Neural Netw.
3 (5) (1990) 551–560, https://doi.org/10.1016/0893-6080(90)90005-6.
[24] G. Cybenko, Approximation by superpositions of a sigmoidal function, Mathem.
Control Signals Syst. 2 (4) (1989) 303–314, https://doi.org/10.1007/
BF02551274.
[25] J. Sanders, E. Kandrot, CUDA By example: an Introduction to General-Purpose
GPU Programming, Addison-Wesley Professional, 2010. ISBN: 9780131387683.
[26] N.P. Jouppi, C. Young, N. Patil, D. Patterson, G. Agrawal, R. Bajwa, In-datacenter
performance analysis of a tensor processing unit, in: Proceedings of the 44th
annual International Symposium on Computer Architecture, 2017, pp. 1–12,
https://doi.org/10.1145/3079856.3080246.
[27] N.P. Jouppi, C. Young, N. Patil, D. Patterson. Patterson, Motivation for and
evaluation of the first tensor processing unit, in: the 51st annual IEEE/ACM
While AI technology has already shown great potential in the field of
computational analysis in civil engineering, it is still in its early stages of
development. With continued advancements in AI algorithms and
computing power, the future of this field is promising, with the potential
to significantly promote the informatization and automation of engi­
neering design and construction in the building and infrastructure
industry.
Declaration of Competing Interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Data availability
Data will be made available on request.
Acknowledgement
The authors gratefully acknowledge the financial support provided
by the National Natural Science Foundation of China (Grant No.
52293433, 52121005), the China National Postdoctoral Program for
Innovative Talents (Award No. BX20220177) and the China Post­
doctoral Science Foundation (Grant No. 2022M711864).
Appendix: Literature list for the review
For the convenience of readers, we have included a comprehensive
list of all the collected papers reviewed in this paper in Table A1.
References
[1] H. Zhang, Q.L. Qi, F. Tao, A multi-scale modeling method for digital twin shopfloor, J. Manuf. Syst. 62 (2022) 417–428, https://doi.org/10.1016/j.
jmsy.2021.12.011.
[2] W. Wang, H. Guo, X. Li, S. Tang, Y. Li, L. Xie, Z. Lv, BIM Information integration
based VR modeling in digital twins in industry 5.0, J. Ind. Inf. Integr. 28 (2022),
100351, https://doi.org/10.1016/j.jii.2022.100351.
[3] A. Sharma, E. Kosasih, J. Zhang, A. Brintrup, A. Calinescu, Digital twins: state of
the art theory and practice, challenges, and open research questions, J. Ind. Inf.
Integr. (2022), 100383, https://doi.org/10.1016/j.jii.2022.100383.
[4] O.C. Zienkiewicz, R.L. Taylor, J.Z. Zhu, The Finite Element method: Its Basis and
Fundamentals, sixth edition, Elsevier Butterworth-Heinemann, 2005. ISBN:
0750663200.
17
C. Wang et al.
[28]
[29]
[30]
[31]
[32]
[33]
[34]
[35]
[36]
[37]
[38]
[39]
[40]
[41]
[42]
[43]
[44]
[45]
[46]
[47]
[48]
[49]
[50]
Journal of Industrial Information Integration 33 (2023) 100470
International Symposium on Microarchitecture 38, 2018, pp. 10–19, https://doi.
org/10.1109/MM.2018.032271057.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin,
N. Gimelshin, L. Antiga, A. Desmison, A. Köpf, E. Yang, Z. DeVito, M. Raison,
A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, S. Chintala, Pytorch: an
imperative style, high-performance deep learning library, Adv. Neural Inf.
Process Syst. 32 (2019) 8026–8037, in: https://proceedings.neurips.cc/paper
/2019/file/bdbca288fee7f92f2bfa9f7012727740-Paper.pdf.
M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat,
G. Irving, M. Isard, M. Kudlur, J. Levengerg, R. Monga, S. Moore, D.G. Murray,
B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, X. Zheng,
Tensorflow: a system for large-scale machine learning, in: 12th USENIX
Symposium on Operating Systems Design and Implementation (OSDI16), 2016,
pp. 265–283. https://www.usenix.org/conference/osdi16/technical-sessions/pr
esentation/abadi.
C. Zhang, Y. Lu, Study on artificial intelligence: the state of the art and future
prospects, J. Ind. Inf. Integr. 23 (2021), 100224, https://doi.org/10.1016/j.
jii.2021.100224.
C. Wanigasekara, E. Oromiehie, A. Swain, B.G. Prusty, S.K. Nguang, Machine
learning-based inverse predictive model for AFP based thermoplastic composites,
J. Ind. Inf. Integr. 22 (2021), 100197, https://doi.org/10.1016/j.jii.2020.100197.
Z. Li, R. Burgueño, Structural information integration for predicting damages in
bridges, J. Ind. Inf. Integr. 15 (2019) 174–182, https://doi.org/10.1016/j.
jii.2018.11.004.
T.D. Akinosho, L.O. Oyedele, M. Bilal, A.O. Ajayi, M.D. Delgado, O.O. Akinade, A.
A. Ahmed, Deep learning in the construction industry: a review of present status
and future innovations, J. Build. Eng. 32 (2020), 101827, https://doi.org/
10.1016/j.jobe.2020.101827.
Y. Pan, L. Zhang, Roles of artificial intelligence in construction engineering and
management: a critical review and future trends, Autom. Constr. 122 (2021),
103517, https://doi.org/10.1016/j.autcon.2020.103517.
Y.F. Liu, X. Nie, J.S. Fan, X.G. Liu, Image-based crack assessment of bridge piers
using unmanned aerial vehicles and three-dimensional scene reconstruction,
Comput. Aided Civ. Infrastruct. Eng. 35 (5) (2020) 511–529, https://doi.org/
10.1111/mice.12501.
J.Z. Xin, Y. Jiang, J.T. Zhou, L.L. Peng, S.Y. Liu, Q.Z. Tang, Bridge deformation
prediction based on SHM data using improved VMD and conditional KDE, Eng.
Struct. 261 (2022), 114285, https://doi.org/10.1016/j.engstruct.2022.114285.
Y. Yang, F. Nan, P. Yang, Effective multilayer hybrid classification approach for
automatic bridge health assessment on large-scale uncertain data, J. Ind. Inf.
Integr. 24 (2021), 100234, https://doi.org/10.1016/j.jii.2021.100234.
J.Z.Xin Q.Tang, Y. Jiang, J.T. Zhou, S.J. Li, Z.Y. Chen, Novel identification
technique of moving loads using the random response power spectral density and
deep transfer learning, Measurement (2022), 111120, https://doi.org/10.1016/j.
measurement.2022.111120.
V. Ewald, R.S. Venkat, A. Asokkumar, Perception modelling by invariant
representation of deep learning for automated structural diagnostic in aircraft
maintenance: a study case using DeepSHM, Mech. Syst. Signal Process. 165
(2022), 108153, https://doi.org/10.1016/j.ymssp.2021.108153.
F.J. Pallarés, M. Betti, G. Bartoli, L. Pallarés, Structural health monitoring (SHM)
and Nondestructive testing (NDT) of slender masonry structures: a practical
review, Constr. Build. Mater. 297 (2021), 123768, https://doi.org/10.1016/j.
conbuildmat.2021.123768.
A. Dogan, D. Birant, Machine learning and data mining in manufacturing, Expert
Syst. Appl. 166 (2021), 114060, https://doi.org/10.1016/j.eswa.2020.114060.
V.R. Gharehbaghi, E.N. Farsangi, M. Noori, T.Y. Yang, S.F. Li, A. Nguyen,
C. Málaga-Chuquitaype, P. Gardoni, S. Mirjalili, A critical review on structural
health monitoring: definitions, methods, and perspectives, Arch. Comput. Meth.
Eng. (2021) 1–27, https://doi.org/10.1007/s11831-021-09665-9.
H.B. Chen, X. Nie, S.Y. Gan, Y.D. Zhao, H.H. Qiu, Interfacial imperfection
detection for steel-concrete composite structures using NDT techniques: a stateof-the-art review, Eng. Struct. 245 (2021), 112778, https://doi.org/10.1016/j.
engstruct.2021.112778.
H. Momeni, A. Ebrahimkhanlou, High-dimensional data analytics in structural
health monitoring and non-destructive evaluation: a review paper, Smart Mater.
Struct. 31 (2022), 043001, https://doi.org/10.1088/1361-665X/ac50f4.
A.V.L.N. Sujith, G.S. Sajja, V. Mahalakshmi, S. Nuhmani, B. Prasanalakshmi,
Systematic review of smart health monitoring using deep learning and artificial
intelligence, Neurosci. Inform. 2 (3) (2022), 100028, https://doi.org/10.1016/j.
neuri.2021.100028.
H. Adeli, Neural networks in civil engineering: 1989-2000, Comput. Aided Civ.
Infrastruct. Eng. 16 (2) (2001) 126–142, https://doi.org/10.1111/08859507.00219.
A.T.G. Tapeh, M.Z. Naser, Artificial intelligence, machine learning, and deep
learning in structural engineering: a scientometrics review of trends and best
practices, Arch. Comput. Meth. Eng. (2022) 1–45, https://doi.org/10.1007/
s11831-022-09793-w.
R. Ding, M.X. Tao, J.G. Nie, Y.L. Mo, Shear deformation and sliding-based fiber
beam-column model for seismic analysis of reinforced concrete coupling beams,
J. Struct. Eng. 142 (7) (2016), 04016032, https://doi.org/10.1061/(ASCE)
ST.1943-541X.0001478.
Q.Z. Liu, Z.Z. Zhao, X.Z. Lu, J.R. Qian, Simulation methods for hysteretic curve of
steel braces, Jianzhu Jiegou Xuebao 41 (08) (2011) 63–67, https://doi.org/
10.19701/j.jzjg.2011.08.013. +39in Chinese.
C. Liu, Y. Yang, J.J. Wang, J.S. Fan, M.X. Tao, Y.L. Mo, Biaxial reinforced concrete
constitutive models for implicit and explicit solvers with reduced mesh
[51]
[52]
[53]
[54]
[55]
[56]
[57]
[58]
[59]
[60]
[61]
[62]
[63]
[64]
[65]
[66]
[67]
[68]
[69]
[70]
[71]
[72]
[73]
[74]
[75]
[76]
18
sensitivity, Eng. Struct. 219 (2020), 110880, https://doi.org/10.1016/j.
engstruct.2020.110880.
K. Maekawa, H. Okamura, A. Pimanmas, Non-linear Mechanics of Reinforced
Concrete, CRC Press, 2003. ISBN: 0367865556.
S. Boyd, S.P. Boyd, L. Vandenberghe, Convex Optimization, Cambridge university
press, 2004. ISBN: 0521833787.
J.C. Simo, R.L. Taylor, A return mapping algorithm for plane stress elastoplasticity, Int. J. 22 (3) (1986) 649–670, https://doi.org/10.1002/
nme.1620220310.
S. Pech, M. Lukacevic, J. Füssl, A robust multisurface return-mapping algorithm
and its implementation in Abaqus, Finite Elem. Anal. Des. 190 (2021), 103531,
https://doi.org/10.1016/j.finel.2021.103531.
L.Y. Xu L, X. Nie, J.S. Fan, Cyclic hardening and softening behavior of the low
yield point steel BLY160: experimental response and constitutive modeling, Int. J.
Plast. 78 (2016) 44–63, https://doi.org/10.1016/j.ijplas.2015.10.009.
C. Wang, L.Y. Xu, J.S. Fan, Cyclic softening behavior of structural steel with strain
range dependence, J. Constr. Steel Res. 181 (2021), 106658, https://doi.org/
10.1016/j.jcsr.2021.106658.
G.W. Zhang, Q.Q. Fan, Z. Lu, Z.W. Zhao, Z.S. Sun, Experimental and numerical
study on the seismic performance of rocking steel frames with different joints
under earthquake excitation, Eng. Struct. 220 (2020), 110974, https://doi.org/
10.1016/j.engstruct.2020.110974.
S. Ghazizadeh, C.A. Cruz-Noguez, Y. Li, Numerical study of hybrid GFRP-steel
reinforced concrete shear walls and SFRC walls, Eng. Struct. 180 (2019) 700–712,
https://doi.org/10.1016/j.engstruct.2018.11.080.
J.M. Cai, J.L. Pan, J.W. Tan, B. Vandevyvere, X.P. Li, Nonlinear analysis of ECCencased CFST columns under axial compression, J. Build. Eng. 31 (2020),
101401, https://doi.org/10.1016/j.jobe.2020.101401.
F.X. Hu, G. Shi, Constitutive model for full-range cyclic behavior of high strength
steels without yield plateau, Constr. Build. Mater. 162 (2018) 596–607, https://
doi.org/10.1016/j.conbuildmat.2017.11.128.
L.Y. Xu, J.S. Fan, Y. Yang, C. Wang, Ratcheting assessment of low yield point steel
BLY160: experimental analysis and constitutive modelling, Mech. Mater. 148
(2020), 103460, https://doi.org/10.1016/j.mechmat.2020.103460.
J.J. Zeng, Y.W. Zheng, F. Liu, Y.C. Guo, C. Hou, Behavior of FRP Ring-Confined
CFST columns under axial compression, Compos. Struct. 257 (2021), 113166,
https://doi.org/10.1016/j.compstruct.2020.113166.
Y.F. An, L.H. Han, X.L. Zhao, Behaviour and design calculations on very slender
thin-walled CFST columns, Thin Walled Struct. 53 (2012) 161–175, https://doi.
org/10.1016/j.tws.2012.01.011.
R. Purba, M. Bruneau, Finite-element investigation and design recommendations
for perforated steel plate shear walls, J. Struct. Eng. 135 (11) (2009) 1367–1376,
https://doi.org/10.1061/(ASCE)ST.1943-541X.0000061.
F. Rosenblatt, Principles of neurodynamics. Perceptrons and the theory of brain
mechanisms, Cornell Aeronautical Lab (1961). https://apps.dtic.mil/sti/pdfs
/AD0256583.pdf.
V. Nair, G.E. Hinton, Rectified linear units improve restricted boltzmann
machines, in: Proceedings of the 27th International Conference on Machine
Learning, 2010, pp. 807–814, https://doi.org/10.5555/3104322.3104425, dl.
acm.org/doi/.
B.E. Boser, I.M. Guyon, V.N. Vapnik, A training algorithm for optimal margin
classifiers, in: Proceedings of the fifth annual Workshop on Computational
Learning Theory, 1992, pp. 144–152, https://doi.org/10.1145/130385.130401.
N. Cristianini, J. Shawe-Taylor, An Introduction to Support Vector Machines and
Other Kernel-Based Learning Methods, Cambridge university press, 2000. ISBN:
0521780195.
N. Patil, R. Lathi, V. Chitre, Comparison of C5. 0 & CART classification algorithms
using pruning technique, Int. J. Eng. Res. Sci. Technol. 1 (4) (2012) 1–5. https
://www.ijert.org/research/comparison-of-c5.0-cart-classification-algorithms
-using-pruning-technique-IJERTV1IS4104.pdf.
P. Sollich, A. Krogh, Learning with ensembles: how overfitting can be useful, in:
Advances in Neural Information Processing Systems, 8, 1995, in: https://procee
dungs.neurips.cc/paper/1995/file/1019c8091693ef5c5f55970346633f92-Paper.
pdf.
F. Pedregosa, G. Varoquaux, A. Gramfort, Scikit-learn: machine learning in
Python, J. Mach. Learn. Res. 12 (2011) 2825–2830, https://doi.org/10.48550/
arXiv.1201.0490.
A. Behnood, E.M. Golafshani, Predicting the compressive strength of silica fume
concrete using hybrid artificial neural network with multi-objective grey wolves,
J. Clean. Prod. 202 (2018) 54–64, https://doi.org/10.1016/j.
jclepro.2018.08.065.
M.A. Getahun, S.M. Shitote, Z.C.A. Gariy, Artificial neural network based
modelling approach for strength prediction of concrete incorporating agricultural
and construction wastes, Constr. Build. Mater. 190 (2018) 517–525, https://doi.
org/10.1016/j.conbuildmat.2018.09.097.
D.K. Bui, T. Nguyen, J.S. Chou, H. Nguyen-Xuan, T. DucNgo, A modified firefly
algorithm-artificial neural network expert system for predicting compressive and
tensile strength of high-performance concrete, Constr. Build. Mater. 180 (2018)
320–333, https://doi.org/10.1016/j.conbuildmat.2018.05.201.
L. Yang, C.C. Qi, X.S. Lin, J.W. Li, X.J. Dong, Prediction of dynamic increase
factor for steel fiber reinforced concrete using a hybrid artificial intelligence
model, Eng. Struct. 189 (2019) 309–318, https://doi.org/10.1016/j.
engstruct.2019.03.105.
D.W. Abueidda, M. Almasri, R. Ammourah, U. Ravaioli, I.M. Jasiuk, N.A. Sobh,
Prediction and optimization of mechanical properties of composites using
C. Wang et al.
[77]
[78]
[79]
[80]
[81]
[82]
[83]
[84]
[85]
[86]
[87]
[88]
[89]
[90]
[91]
[92]
[93]
[94]
[95]
[96]
[97]
[98]
[99]
Journal of Industrial Information Integration 33 (2023) 100470
convolutional neural networks, Compos. Struct. 227 (2019), 111264, https://doi.
org/10.1016/j.compstruct.2019.111264.
D.C. Feng, Z.T. Liu, X.D. Wang, Y. Chen, J.Q. Chang, D.F. Wei, Z.M. Jiang,
Machine learning-based compressive strength prediction for concrete: an
adaptive boosting approach, Constr. Build. Mater. 230 (2020), 117000, https://
doi.org/10.1016/j.conbuildmat.2019.117000.
M.R. Kaloop, D. Kumar, P. Samui, J.W. Hu, D. Kim, Compressive strength
prediction of high-performance concrete using gradient tree boosting machine,
Constr. Build. Mater. 264 (2020), 120198, https://doi.org/10.1016/j.
conbuildmat.2020.120198.
R.L. Zhang, X.H. Xue, A predictive model for the bond strength of near-surfacemounted FRP bonded to concrete, Compos. Struct. 262 (2021), 113618, https://
doi.org/10.1016/j.compstruct.2021.113618.
S.R. Salimbahrami, R. Shakeri, Experimental investigation and comparative
machine-learning prediction of compressive strength of recycled aggregate
concrete, Soft Comput. 25 (2) (2021) 919–932, https://doi.org/10.1007/s00500021-05571-1.
H.B. Ly, T.A. Nguyen, H.V.T. Mai, V.Q. Tran, Development of deep neural
network model to predict the compressive strength of rubber concrete, Constr.
Build. Mater. 301 (2021), 124081, https://doi.org/10.1016/j.
conbuildmat.2021.124081.
S.Z. Chen, D.C. Feng, W.S. Han, G. Wu, Development of data-driven prediction
model for CFRP-steel bond strength by implementing ensemble learning
algorithms, Constr. Build. Mater. 303 (2021), 124470, https://doi.org/10.1016/j.
conbuildmat.2021.124470.
M.C. Kang, D.Y. Yoo, R. Gupta, Machine learning-based prediction for
compressive and flexural strengths of steel fiber-reinforced concrete, Constr.
Build. Mater. 266 (2021), 121117, https://doi.org/10.1016/j.
conbuildmat.2020.121117.
H. Naderpour, O. Poursaeidi, M. Ahmadi, Shear resistance prediction of concrete
beams reinforced by FRP bars using artificial neural networks, Measurement 126
(2018) 299–308, https://doi.org/10.1016/j.measurement.2018.05.051.
S. Mangalathu, J.S. Jeon, Classification of failure mode and prediction of shear
strength for reinforced concrete beam-column joints using machine learning
techniques, Eng. Struct. 160 (2018) 85–94, https://doi.org/10.1016/j.
engstruct.2018.01.008.
A.A.H. Alwanas, A.A. Al-Musawi, S.Q. Salih, H. Tao, M. Ali, Z.M. Yaseen, Loadcarrying capacity and mode failure simulation of beam-column joint connection:
application of self-tuning machine learning model, Eng. Struct. 194 (2019)
220–229, https://doi.org/10.1016/j.engstruct.2019.05.048.
M.Z. Naser, AI-based cognitive framework for evaluating response of concrete
structures in extreme conditions, Eng. Appl. Artif. Intell. 81 (2019) 437–449,
https://doi.org/10.1016/j.engappai.2019.03.004.
B. Keshtegar, M. Bagheri, Z.M. Yaseen, Shear strength of steel fiber-unconfined
reinforced concrete beam simulation: application of novel intelligent model,
Compos. Struct. 212 (2019) 230–242, https://doi.org/10.1016/j.
compstruct.2019.01.004.
M. Abambres, E.O.L. Lantsoght, Neural network-based formula for shear capacity
prediction of one-way slabs under concentrated loads, Eng. Struct. 211 (2020),
110501, https://doi.org/10.1016/j.engstruct.2020.110501.
G.N. Zhang, Z.H. Ali, M.S. Aldlemy, M.H. Mussa, S.Q. Salih, M.M. Hameed, Z.
S. Al-Khafaji, Z.M. Yaseen, Reinforced concrete deep beam shear strength
capacity modelling using an integrative bio-inspired algorithm with an artificial
intelligence model, Eng. Comput. (2020) 1–14, https://doi.org/10.1007/s00366020-01137-1.
D.C. Feng, B. Cetiner, M.R. Azadi Kakavand, E. Taciroglu, Data-driven approach
to predict the plastic hinge length of reinforced concrete columns and its
application, J. Struct. Eng. 147 (2) (2021), 040203, https://doi.org/10.1061/
(ASCE)ST.1943-541X.0002852.
A. Kaveh, A. Dadras Eslamlou, S.M. Javadi, N. Geran Malek, Machine learning
regression approaches for predicting the ultimate buckling load of variablestiffness composite cylinders, Acta Mech. 232 (3) (2021) 921–931, https://doi.
org/10.1007/s00707-020-02878-2.
Q.V. Vu, V.H. Truong, H.T. Thai, Machine learning-based prediction of CFST
columns using gradient tree boosting algorithm, Compos. Struct. 259 (2021),
113505, https://doi.org/10.1016/j.compstruct.2020.113505.
M. Zarringol M, H.T. Thai, M.Z. Naser, Application of machine learning models
for designing CFST columns, J. Constr. Steel Res. 185 (2021), 106856, https://
doi.org/10.1016/j.jcsr.2021.106856.
C. Hou, X.G. Zhou, Strength prediction of circular CFST columns through
advanced machine learning methods, J. Build. Eng. 51 (2022), 104289, https://
doi.org/10.1016/j.jobe.2022.104289.
M. Alam, N. Sultana, S.M. Zakir Hossain, M.S. Islam, Hybrid intelligence
modeling for estimating shear strength of FRP reinforced concrete members,
Neural Comput. Appl. (2022) 1–11, https://doi.org/10.1007/s00521-021-067910.
K. Morfidis, K. Kostinakis, Seismic parameters’ combinations for the optimum
prediction of the damage state of R/C buildings using neural networks, Adv. Eng.
Softw. 106 (2017) 1–16, https://doi.org/10.1016/j.advengsoft.2017.01.001.
H. Sun, H. Burton, J. Wallace, Reconstructing seismic response demands across
multiple tall buildings using kernel-based machine learning methods, Struct.
Control Health Monit. 26 (7) (2019) e2359, https://doi.org/10.1002/stc.2359.
H. Huang, H.V. Burton, Classification of in-plane failure modes for reinforced
concrete frames with infills using machine learning, J. Build. Eng. 25 (2019),
100767, https://doi.org/10.1016/j.jobe.2019.100767.
[100] S.H. Hwang, S. Mangalathu, J. Shin, J.S. Jeon, Machine learning-based
approaches for seismic demand and collapse of ductile reinforced concrete
building frames, J. Build. Eng. 34 (2021), 101905, https://doi.org/10.1016/j.
jobe.2020.101905.
[101] X.Q. Guan, H. Burton, M. Shokrabadi, Z.X. Yi, Seismic drift demand estimation for
steel moment frame buildings: from mechanics-based to data-driven models,
J. Struct. Eng. 147 (6) (2021), 04021058, https://doi.org/10.1061/(ASCE)
ST.1943-541X.0003004.
[102] S.J. Zhu, M. Ohsaki, X.N. Guo, Prediction of non-linear buckling load of imperfect
reticulated shell using modified consistent imperfection and machine learning,
Eng. Struct. 226 (2021), 111374, https://doi.org/10.1016/j.
engstruct.2020.111374.
[103] M.Z. Esteghamati, M.M. Flint, Developing data-driven surrogate models for
holistic performance-based assessment of mid-rise RC frame buildings at early
design, Eng. Struct. 245 (2021), 112971, https://doi.org/10.1016/j.
engstruct.2021.112971.
[104] G.F. Sirca Jr, H. Adeli, Neural network model for uplift load capacity of metal roof
panels, J. Struct. Eng. 127 (11) (2001) 1276–1285, https://doi.org/10.1061/
(ASCE)0733-9445(2001)127:11(1276).
[105] M. Sarveghadi, A.H. Gandomi, H. Bolandi, A.H. Alavi, Development of prediction
models for shear strength of SFRCB using a machine learning approach, Neural
Comput. Appl. 31 (2019) 2085–2094, https://doi.org/10.1007/s00521-0151997-6.
[106] S. Lai, M. Serra, Concrete strength prediction by means of neural network, Constr.
Build. Mater. 11 (2) (1997) 93–98, https://doi.org/10.1016/S0950-0618(97)
00007-X.
[107] H.I. Erdal, O. Karakurt, E. Namli, High performance concrete compressive
strength forecasting using ensemble models based on discrete wavelet transform,
Eng. Appl. Artif. Intell. 26 (4) (2013) 1246–1254, https://doi.org/10.1016/j.
engappai.2012.10.014.
[108] K.Z. Yan, H.B. Xu, G.H. Shen, P. Liu, Prediction of splitting tensile strength from
cylinder compressive strength of concrete by support vector machine, Adv. Mater.
Sci. Eng. 2013 (2013), https://doi.org/10.1155/2013/597257.
[109] F. Khademi, M. Akbari, S.M. Jamal, et al., Multiple linear regression, artificial
neural network, and fuzzy logic prediction of 28 days compressive strength of
concrete, Front. Struct. Civil Eng. 11 (2017) 90–99, https://doi.org/10.1007/
s11709-016-0363-9.
[110] S.D. Shen, P. Pan, Z.Z. He, G.Q. Xiao, X.J. Li, Parametric analysis and new design
formulas of a prefabricated energy-dissipating composite slotted shear wall,
Earthq. Eng. Struct. Dyn. 50 (8) (2021) 2115–2133, https://doi.org/10.1002/
eqe.3439.
[111] J. Devlin, M.W. Chang, K. Lee, K. Toutanova. Bert: Pre-training of deep
bidirectional transformers for language understanding. arXiv preprint arXiv:
1810.04805, (2018). https://doi.org/10.48550/arXiv.1810.04805.
[112] S. Hochreiter, J. Schmidhuber, Long short-term memory, Neural Comput. 9 (8)
(1997) 1735–1780, https://doi.org/10.1162/neco.1997.9.8.1735.
[113] J. Chung, C. Gulcehre, K.H. Cho, Y. Bengio, Empirical evaluation of gated
recurrent neural networks on sequence modeling, in: Proceedings of the 28th
Conference on Neural Information Processing Systems, 2014, https://doi.org/
10.48550/arXiv.1412.3555.
[114] C. Wang, L.Y. Xu, J.S. Fan, A general deep learning framework for historydependent response prediction based on UA-Seq2Seq model, Comput. Methods
Appl. Mech. Eng. 372 (2020), 113357, https://doi.org/10.1016/j.
cma.2020.113357.
[115] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser,
I. Polosukhin, Attention is all you need, Adv Neural Inf Process Syst 30 (2017)
5998–6008, in: https://proceedings.neurips.cc/paper/2017/file/3f5ee2435
47dee91fbd053c1c4a845aa-Paper.pdf.
[116] S. Jung, J. Ghaboussi, Neural network constitutive model for rate-dependent
materials, Comput. Struct. 84 (15–16) (2006) 955–963, https://doi.org/10.1016/
j.compstruc.2006.02.015.
[117] F. Ghavamian, A. Simone, Accelerating multiscale finite element simulations of
history-dependent materials using a recurrent neural network, Comput. Methods
Appl. Mech. Eng. 357 (2019), 112594, https://doi.org/10.1016/j.
cma.2019.112594.
[118] H. Wang, Research On Concrete Creep Based On Ensemble Learning and LSTM
Artificial Intelligence Algorithms, Beijing Jiaotong University, Beijing, 2020,
https://doi.org/10.26944/d.cnki.gbfju.2020.001406 in Chinese.
[119] M.B. Gorji, M. Mozaffar, J.N. Heidenreich, J. Cao, D. Mohr, On the potential of
recurrent neural networks for modeling path dependent plasticity, J. Mech. Phys.
Solids 143 (2020), 103972, https://doi.org/10.1016/j.jmps.2020.103972.
[120] Y.H. Feng, J. He, G.F. Han, S.H. Li, Z.Q. Lin, Deep learning predicting method and
modeling of plastic constitutive relation of steel metal, Suxing Gongcheng Xuebao
28 (6) (2021) 34–46. Chinese, https://kns.cnki.net/kcms/detail/detail.aspx?
FileName=SXGC202106007&DbName=DKFX2021.
[121] J.J. Wang, C. Wang, J.S. Fan, Y.L. Mo, A deep learning framework for constitutive
modeling based on temporal convolutional network, J. Comput. Phys. 449
(2022), 110784, https://doi.org/10.1016/j.jcp.2021.110784.
[122] M. Bartošák, Using machine learning to predict lifetime under isothermal lowcycle fatigue and thermo-mechanical fatigue loading, Int. J. 163 (2022), 107067,
https://doi.org/10.1016/j.ijfatigue.2022.107067.
[123] N.H. Christiansen, J. Høgsberg, O. Winther, Artificial neural networks for
nonlinear dynamic response simulation in mechanical systems, Proc. 24th Nordic
Seminar Comput. Mechan. 24 (2011) 77–80. https://backend.orbit.dtu.dk/ws/po
rtalfiles/portal/6282759/prod21321948946741.NSCM-24_main%5B1%5D.pdf.
19
C. Wang et al.
Journal of Industrial Information Integration 33 (2023) 100470
[124] N.D. Lagaros, M. Papadrakakis, Neural network based prediction schemes of the
non-linear seismic response of 3D buildings, Adv. Eng. Software 44 (1) (2012)
92–115, https://doi.org/10.1016/j.advengsoft.2011.05.033.
[125] T. Kim, O.S. Kwon, J. Song, Response prediction of nonlinear hysteretic systems
by deep neural networks, Neural Netw. 111 (2019) 1–10, https://doi.org/
10.1016/j.neunet.2018.12.005.
[126] A. Koeppe, F. Bamer, B. Markert, An efficient Monte Carlo strategy for elastoplastic structures based on recurrent neural networks, Acta Mech. 230 (9) (2019)
3279–3293, https://doi.org/10.1007/s00707-019-02436-5.
[127] R.Y. Zhang, Y. Liu, H. Sun, Physics-guided convolutional neural network
(PhyCNN) for data-driven seismic response modeling, Eng. Struct. 215 (2020),
110704, https://doi.org/10.1016/j.engstruct.2020.110704.
[128] B.K. Oh, Y. Park, H.S. Park, Seismic response prediction method for building
structures using convolutional neural network, Struct. Control Health Monit. 27
(5) (2020) e2519, https://doi.org/10.1002/stc.2519.
[129] P. Huang, Z. Chen, Deep learning for nonlinear seismic responses prediction of
subway station, Eng. Struct. 244 (2021), 112735, https://doi.org/10.1016/j.
engstruct.2021.112735.
[130] A.A. Torky, S. Ohno, Deep learning techniques for predicting nonlinear multicomponent seismic responses of structural buildings, Comput. Struct. 252 (2021),
106570, https://doi.org/10.1016/j.compstruc.2021.106570.
[131] J.Y. Xue, G. Ou, Predicting wind-induced structural response with LSTM in
transmission tower-line system, Smart Struct. Syst. 28 (3) (2021), https://doi.org/
10.12989/sss.2021.28.0.000.
[132] Z.K. Xu, J. Chen, J.X. Shen, M.J. Xiang, Recursive long short-term memory
network for predicting nonlinear structural seismic response, Eng. Struct. 250
(2022), 113406, https://doi.org/10.1016/j.engstruct.2021.113406.
[133] I. Sutskever, O. Vinyals, Q.V. Le, Sequence to sequence learning with neural
networks, Proc. 28th Conference Neural Inform. Process. Syst. 27 (2014)
3104–3112, in: https://proceedings.neurips.cc/paper/2014/file/a14ac55a4f274
72c5d894ec1c3c743d2-Paper.pdf.
[134] Y. Xu, X. Lu, Y. Fei, Y. Huang. Data-driven hysteretic behavior simulation based
on weighted stacked pyramid neural network architecture. (2022), arXiv:
2206.03990. https://arxiv.org/abs/2206.03990.
[135] X. Jiang, H. Adeli, Dynamic wavelet neural network for nonlinear identification of
highrise buildings, Comput. Aided Civ. Infrastruct. Eng. 20 (5) (2005) 316–330,
https://doi.org/10.1111/j.1467-8667.2005.00399.x.
[136] H. Luo, S.G. Paal, Machine learning–based backbone curve model of reinforced
concrete columns subjected to cyclic loading reversals, J. Comput. Civil Eng. 32
(5) (2018), 04018042, https://doi.org/10.1061/(ASCE)CP.1943-5487.0000787.
[137] Z.L. Liu, S.C. Li, Development of an ANN-based lumped plasticity model of RC
columns using historical pseudo-static cyclic test data, Appl. Sci. 9 (20) (2019)
4263, https://doi.org/10.3390/app9204263.
[138] A.X.Guo Z.L.Liu, Empirical-based support vector machine method for seismic
assessment and simulation of reinforced concrete columns using historical cyclic
tests, Eng. Struct. 237 (2021), 112141, https://doi.org/10.1016/j.
engstruct.2021.112141.
[139] X.L. Han, R.P. Feng, J. Ji, Research on parameters of the RC beam lumped plastic
hinge model based on deep learning, Eng. Mech. 38 (11) (2021) 160–169, in
Chinese, https://kns.cnki.net/kcms/detail/detail.aspx?FileName=GCLX202111
017&DbName=CJFQ2021.
[140] M. Zarringol, H.T. Thai, Prediction of the load-shortening curve of CFST columns
using ANN-based models, J. Build. Eng. 51 (2022), 104279, https://doi.org/
10.1016/j.jobe.2022.104279.
[141] W. Wen, C. Zhang, C. Zhai, Rapid seismic response prediction of RC frames based
on deep learning and limited building information, Eng. Struct. 267 (2022),
114638, https://doi.org/10.1016/j.engstruct.2022.114638.
[142] M.H. Soleimani-Babakamali, M.Zaker Esteghamati, Estimating seismic demand
models of a building inventory from nonlinear static analysis using deep learning
methods, Eng. Struct. 266 (2022), 114576, https://doi.org/10.1016/j.
engstruct.2022.114576.
[143] C. Wang, L.H. Song, J.S. Fan, End-to-End Structural analysis in civil engineering
based on deep learning, Autom. Constr. 138 (2022), 104255, https://doi.org/
10.1016/j.autcon.2022.104255.
[144] R. Wang, B. Fu, G. Fu, M. Wang, Deep & cross network for ad click predictions, in:
Proceedings of the ADKDD’17, 2017, pp. 1–7, https://doi.org/10.1145/
3124749.3124754.
[145] K. Choromanski, V. Likhosherstov, D. Dohan, X. Song, G.A. Gane, T. Sarlos,
P. Hawkins, J.Q. Davis, A. Mohiuddin, L. Kaiser, D. Belanger, L.J. Colwell,
A. Weller, Rethinking attention with performers, in: International Conference on
Learning Representations 2021, 2021. https://openreview.net/pdf?id=U
a6zuk0WRH.
[146] D.P. Kingma, J. Ba, Adam: a method for stochastic optimization, in: the 3rd
International Conference for Learning Representations, 2015. https://arxiv.org/
abs/1412.6980.
[147] I.R. Choi, H.G. Park, Steel plate shear walls with various infill plate designs,
J. Struct. Eng. 135 (7) (2009) 785–796, https://doi.org/10.1061/(ASCE)07339445(2009)135:7(785).
[148] H.G. Park, J.H. Kwack, S.W. Jeon, W.K. Kim, I.R. Choi, Framed steel plate wall
behavior under cyclic lateral loading, J. Struct. Eng. 133 (3) (2007) 378–388,
https://doi.org/10.1061/(ASCE)0733-9445(2007)133:3(378).
[149] J.G. Nie, L. Zhu, J.S. Fan, Y.L. Mo, Lateral resistance capacity of stiffened steel
plate shear walls, Thin Walled Struct. 67 (2013) 155–167, https://doi.org/
10.1016/j.tws.2013.01.014.
[150] Y. Maksum, A. Amirli, A. Amangeldi, A. Inkarbekov, Y. Ding, A. Romagnoli,
S. Rustamov, B. Akhmetov, Computational acceleration of topology optimization
[151]
[152]
[153]
[154]
[155]
[156]
[157]
[158]
[159]
[160]
[161]
[162]
[163]
[164]
[165]
[166]
[167]
[168]
[169]
[170]
[171]
[172]
[173]
[174]
[175]
20
using parallel computing and machine learning methods – analysis of research
trends, J. Ind. Inf. Integr. 28 (2022), 100352, https://doi.org/10.1016/j.
jii.2022.100352.
J. Pokojski, K. Szustakiewicz, Ł. Woźnicki, K. Oleksiński, J. Pruszyński, Industrial
application of knowledge-based engineering in commercial CAD /CAE systems,
J. Ind. Inf. Integr. 25 (2022), 100255, https://doi.org/10.1016/j.jii.2021.100255.
F. Demir, Prediction of elastic modulus of normal and high strength concrete by
artificial neural networks, Constr. Build. Mater. 22 (7) (2008) 1428–1435,
https://doi.org/10.1016/j.conbuildmat.2007.04.004.
Z. Dahou, Z. Mehdi Sbartaï, A. Castel, F. Ghomari, Artificial neural network model
for steel–concrete bond prediction, Eng. Struct. 31 (8) (2009) 1724–1733,
https://doi.org/10.1016/j.engstruct.2009.02.010.
J. Sobhani, M. Najimi, A.R. Pourkhorshidi, T. Parhizkar, Prediction of the
compressive strength of no-slump concrete: a comparative study of regression,
neural network and ANFIS models, Constr. Build. Mater. 24 (5) (2010) 709–718,
https://doi.org/10.1016/j.conbuildmat.2009.10.037.
J. Tinoco, A. Gomes Correia, P. Cortez, Application of data mining techniques in
the estimation of the uniaxial compressive strength of jet grouting columns over
time, Constr. Build. Mater. 25 (3) (2011) 1257–1262, https://doi.org/10.1016/j.
conbuildmat.2010.09.027.
E.M. Golafshani, A. Rahai, M.H. Sebt, H. Akbarpour, Prediction of bond strength
of spliced steel bars in concrete using artificial neural network and fuzzy logic,
Constr. Build. Mater. 36 (2012) 411–418, https://doi.org/10.1016/j.
conbuildmat.2012.04.046.
Z. Duan, S. Kou, C. Poon, Using artificial neural networks for predicting the elastic
modulus of recycled aggregate concrete, Constr. Build. Mater. 44 (2013)
524–532, https://doi.org/10.1016/j.conbuildmat.2013.02.064.
N. Ghafoori, M. Najimi, J. Sobhani, M.A. Aqel, Predicting rapid chloride
permeability of self-consolidating concrete: a comparative study on statistical and
neural network models, Constr. Build. Mater. 44 (2013) 381–390, https://doi.
org/10.1016/j.conbuildmat.2013.03.039.
L. Bal, F. Buyle-Bodin, Artificial neural network for predicting drying shrinkage of
concrete, Constr. Build. Mater. 38 (2013) 248–254, https://doi.org/10.1016/j.
conbuildmat.2012.08.043.
J. Chou, C. Tsai, A. Pham, Y. Lu, Machine learning in concrete strength
simulations: multi-nation data analytics, Constr. Build. Mater. 73 (2014)
771–780, https://doi.org/10.1016/j.conbuildmat.2014.09.054.
E.M. Golafshani, A. Rahai, M.H. Sebt, Artificial neural network and genetic
programming for predicting the bond strength of GFRP bars in concrete, Mater.
Struct. 48 (2015) 1581–1602, https://doi.org/10.1617/s11527-014-0256-0.
A. Behnood, V. Behnood, M. Modiri Gharehveran, K.E. Alyamac, Prediction of the
compressive strength of normal and high-performance concretes using M5P
model tree algorithm, Constr. Build. Mater. 142 (2017) 199–207, https://doi.org/
10.1016/j.conbuildmat.2017.03.061.
M.H. Rafiei, W.H. Khushefati, R. Demirboga, H. Adeli, Supervised deep restricted
Boltzmann machine for estimation of concrete, ACI Mater. J. 114 (2) (2017) 237,
https://doi.org/10.14359/51689560.
T. Ikumi, E. Galeote, P. Pujadas, A. de la Fuente, R.D. López-Carreño, Neural
network-aided prediction of post-cracking tensile strength of fibre-reinforced
concrete, Comput. Struct. 256 (2021), 106640, https://doi.org/10.1016/j.
compstruc.2021.106640.
B. Keshtegar, A. Gholampour, D. Thai, O. Taylan, N. Trung, Hybrid regression and
machine learning model for predicting ultimate condition of FRP-confined
concrete, Compos. Struct. 262 (2021), 113644, https://doi.org/10.1016/j.
compstruct.2021.113644.
H. Nguyen, T. Vu, T.P. Vo, H. Thai, Efficient machine learning models for
prediction of concrete strengths, Constr. Build. Mater. 266 (2021), 120950,
https://doi.org/10.1016/j.conbuildmat.2020.120950.
C. Zhang, Y. Li, B. Jiang, R. Wang, Y. Liu, L. Jia, Mechanical properties prediction
of composite laminate with FEA and machine learning coupled method, Compos.
Struct. 299 (2022), 116086, https://doi.org/10.1016/j.compstruct.2022.116086.
B.B. Adhikary, H. Mutsuyoshi, Prediction of shear strength of steel fiber RC beams
using neural networks, Constr. Build. Mater. 20 (9) (2006) 801–811, https://doi.
org/10.1016/j.conbuildmat.2005.01.047.
H. Naderpour, A. Kheyroddin, G.G. Amiri, Prediction of FRP-confined
compressive strength of concrete using artificial neural networks, Compos. Struct.
92 (12) (2010) 2817–2829, https://doi.org/10.1016/j.compstruct.2010.04.008.
M.R. Sheidaii, R. Bahraminejad, Evaluation of compression member buckling and
post-buckling behavior using artificial neural network, J. Constr. Steel Res. 70
(2012) 71–77, https://doi.org/10.1016/j.jcsr.2011.10.020.
T.Kalman Šipoš, V. Sigmund, M. Hadzima-Nyarko, Earthquake performance of
infilled frames using neural networks and experimental database, Eng. Struct. 51
(2013) 113–127, https://doi.org/10.1016/j.engstruct.2012.12.038.
T.M. Pham, M.N. Hadi, Predicting stress and strain of FRP-confined square/
rectangular columns using artificial neural networks, J. Compos. Constr. 18 (6)
(2014), 04014019, https://doi.org/10.1061/(ASCE)CC.1943-5614.0000477.
S. Lee, C. Lee, Prediction of shear strength of FRP-reinforced concrete flexural
members without stirrups using artificial neural networks, Eng. Struct. 61 (2014)
99–112, https://doi.org/10.1016/j.engstruct.2014.01.001.
J.S. Jeon, A. Shafieezadeh, R. DesRoches, Statistical models for shear strength of
RC beam-column joints using machine-learning techniques, Earthq. Eng. Struct.
Dyn. 43 (14) (2014) 2075–2095, https://doi.org/10.1002/eqe.2437.
D.T. Vu, N.D. Hoang, Punching shear capacity estimation of FRP-reinforced
concrete slabs using a hybrid machine learning approach, Struct. Infrastruct. Eng.
12 (9) (2016) 1153–1161, https://doi.org/10.1080/15732479.2015.1086386.
C. Wang et al.
Journal of Industrial Information Integration 33 (2023) 100470
[176] A. Cascardi, F. Micelli, M.A. Aiello, An Artificial Neural Networks model for the
prediction of the compressive strength of FRP-confined concrete circular columns,
Eng. Struct. 140 (2017) 199–208, https://doi.org/10.1016/j.
engstruct.2017.02.047.
[177] Z.M. Yaseen, M.T. Tran, S. Kim, T. Bakhshpoori, R.C. Deo, Shear strength
prediction of steel fiber reinforced concrete beam using hybrid intelligence
models: a new approach, Eng. Struct. 177 (2018) 244–255, https://doi.org/
10.1016/j.engstruct.2018.09.074.
[178] X.L. Chen, J.P. Fu, J.L. Yao, J.F. Gan, Prediction of shear strength for squat RC
walls using a hybrid ANN–PSO model, Eng. Comput. 34 (2) (2018) 367–383,
https://doi.org/10.1007/s00366-017-0547-5.
[179] N. Hoang, Estimating punching shear capacity of steel fibre reinforced concrete
slabs using sequential piecewise multiple linear regression and artificial neural
network, Measurement 137 (2019) 58–70, https://doi.org/10.1016/j.
measurement.2019.01.035.
[180] S. Mangalathu, J.S. Jeon, Machine learning–based failure mode recognition of
circular reinforced concrete bridge columns: comparative study, J. Struct. Eng.
145 (10) (2019), 04019104, https://doi.org/10.1061/(ASCE)ST.1943541X.0002402.
[181] V. Tran, D. Thai, S. Kim, Application of ANN in predicting ACC of SCFST column,
Compos. Struct. 228 (2019), 111332, https://doi.org/10.1016/j.
compstruct.2019.111332.
[182] H. Naderpour, M. Mirrashid, Classification of failure modes in ductile and nonductile concrete joints, Eng. Fail. Anal. 103 (2019) 361–375, https://doi.org/
10.1016/j.engfailanal.2019.04.047.
[183] S. Mangalathu, H. Jang, S. Hwang, J. Jeon, Data-driven machine-learning-based
seismic failure mode identification of reinforced concrete shear walls, Eng. Struct.
208 (2020), 110331, https://doi.org/10.1016/j.engstruct.2020.110331.
[184] D. Feng, Z. Liu, X. Wang, Z. Jiang, S. Liang, Failure mode classification and
bearing capacity prediction for reinforced concrete columns based on ensemble
machine learning algorithm, Adv. Eng. Inf. 45 (2020), 101126, https://doi.org/
10.1016/j.aei.2020.101126.
[185] R. Solhmirzaei, H. Salehi, V. Kodur, M. Naser, Machine learning framework for
predicting failure mode and shear capacity of ultra high performance concrete
beams, Eng. Struct. 224 (2020), 111221, https://doi.org/10.1016/j.
engstruct.2020.111221.
[186] D.C. Feng, B. Fu, Shear strength of internal reinforced concrete beam-column
joints: intelligent modeling approach and sensitivity analysis, Adv. Civil Eng.
(2020) 1–19, https://doi.org/10.1155/2020/8850417.
[187] A.A. Al-Musawi, A.A. Alwanas, S.Q. Salih, Z.H. Ali, M.T. Tran, Z.M. Yaseen, Shear
strength of SFRCB without stirrups simulation: implementation of hybrid artificial
intelligence model, Eng. Comput. 36 (1) (2020) 1–11, https://doi.org/10.1007/
s00366-018-0681-8.
[188] V. Tran, D. Thai, D. Nguyen, Practical artificial neural network tool for predicting
the axial compression capacity of circular concrete-filled steel tube columns with
ultra-high-strength concrete, Thin Walled Struct. 151 (2020), 106720, https://
doi.org/10.1016/j.tws.2020.106720.
[189] S. Mangalathu, H. Shin, E. Choi, J. Jeon, Explainable machine learning models for
punching shear strength estimation of flat slabs without transverse reinforcement,
J. Build. Eng. 39 (2021), 102300, https://doi.org/10.1016/j.jobe.2021.102300.
[190] T.G. Wakjira, M.S. Alam, U. Ebead, Plastic hinge length of rectangular RC
columns using ensemble machine learning model, Eng. Struct. 244 (2021),
112808, https://doi.org/10.1016/j.engstruct.2021.112808.
[191] X. Gao, C. Lin, Prediction model of the failure mode of beam-column joints using
machine learning methods, Eng. Fail. Anal. 120 (2021), 105072, https://doi.org/
10.1016/j.engfailanal.2020.105072.
[192] M. Sadegh Barkhordari, M. Tehranizadeh, Response estimation of reinforced
concrete shear walls using artificial neural network and simulated annealing
algorithm, Structures 34 (2021) 1155–1168, https://doi.org/10.1016/j.
istruc.2021.08.053.
[193] J. Rahman, K.S. Ahmed, N.I. Khan, K. Islam, S. Mangalathu, Data-driven shear
strength prediction of steel fiber reinforced concrete beams using machine
learning approach, Eng. Struct. 233 (2021), 111743, https://doi.org/10.1016/j.
engstruct.2020.111743.
[194] D. Feng, W. Wang, S. Mangalathu, G. Hu, T. Wu, Implementing ensemble learning
methods to predict the shear strength of RC deep beams with/without web
reinforcements, Eng. Struct. 235 (2021), 111979, https://doi.org/10.1016/j.
engstruct.2021.111979.
[195] O.B. Olalusi, P.O. Awoyera, Shear capacity prediction of slender reinforced
concrete structures with steel fibers using machine learning, Eng. Struct. 227
(2021), 111470, https://doi.org/10.1016/j.engstruct.2020.111470.
[196] A.S. Bakouregui, H.M. Mohamed, A. Yahia, B. Benmokrane, Explainable extreme
gradient boosting tree-based prediction of load-carrying capacity of FRP-RC
columns, Eng. Struct. 245 (2021), 112836, https://doi.org/10.1016/j.
engstruct.2021.112836.
[197] M. Asif Bin Kabir, A. Sajid Hasan, A. Muntasir Billah, Failure mode identification
of column base plate connection using data-driven machine learning techniques,
Eng. Struct. 240 (2021), 112389, https://doi.org/10.1016/j.
engstruct.2021.112389.
[198] M. Naser, V. Kodur, H. Thai, R. Hawileh, J. Abdalla, V.V. Degtyarev,
StructuresNet and FireNet: benchmarking databases and machine learning
algorithms in structural and fire engineering domains, J. Build. Eng. 44 (2021),
102977, https://doi.org/10.1016/j.jobe.2021.102977.
[199] D. Nguyen, V. Tran, D. Ha, V. Nguyen, T. Lee, A machine learning-based
formulation for predicting shear capacity of squat flanged RC walls, Structures 29
(2021) 1734–1747, https://doi.org/10.1016/j.istruc.2020.12.054.
[200] N. Luat, S.W. Han, K. Lee, Genetic algorithm hybridized with eXtreme gradient
boosting to predict axial compressive capacity of CCFST columns, Compos. Struct.
278 (2021), 114733, https://doi.org/10.1016/j.compstruct.2021.114733.
[201] H. Naderpour, M. Mirrashid, P. Parsa, Failure mode prediction of reinforced
concrete columns using machine learning methods, Eng. Struct. 248 (2021),
113263, https://doi.org/10.1016/j.engstruct.2021.113263.
[202] D.C. Feng, W.J. Wang, S. Mangalathu, E. Taciroglu, Interpretable XGBoost-SHAP
machine-learning model for shear strength prediction of squat RC walls, J. Struct.
Eng. 147 (11) (2021), 04021173, https://doi.org/10.1061/(ASCE)ST.1943541X.0003115.
[203] B. Keshtegar, M.L. Nehdi, R. Kolahchi, N.T. Trung, M. Bagheri, Novel hybrid
machine leaning model for predicting shear strength of reinforced concrete shear
walls, Eng. Comput. (2021) 1–12, https://doi.org/10.1007/s00366-021-01302-0.
[204] B. Keshtegar, M.L. Nehdi, N. Trung, R. Kolahchi, Predicting load capacity of shear
walls using SVR–RSM model, Soft Comput. 112 (2021), 107739, https://doi.org/
10.1016/j.asoc.2021.107739.
[205] M.S. Alam, N. Sultana, S.Z. Hossain, Bayesian optimization algorithm based
support vector regression analysis for estimation of shear capacity of FRP
reinforced concrete members, Soft Comput. 105 (2021), 107281, https://doi.org/
10.1016/j.asoc.2021.107281.
[206] T.T. Le, P.G. Asteris, M.E. Lemonis, Prediction of axial load capacity of
rectangular concrete-filled steel tube columns using machine learning techniques,
Eng. Comput. (2021) 1–34, https://doi.org/10.1007/s00366-021-01461-0.
[207] Q. Xiong, H. Xiong, Q. Kong, X. Ni, Y. Li, C. Yuan, Machine learning-driven
seismic failure mode identification of reinforced concrete shear walls based on
PCA feature extraction, Structures 44 (2022) 1429–1442, https://doi.org/
10.1016/j.istruc.2022.08.089.
[208] H. Zhang, X. Cheng, Y. Li, X. Du, Prediction of failure modes, strength, and
deformation capacity of RC shear walls through machine learning, J. Build. Eng.
50 (2022), 104145, https://doi.org/10.1016/j.jobe.2022.104145.
[209] O.R. de Lautour, P. Omenzetter, Prediction of seismic-induced structural damage
using artificial neural networks, Eng. Struct. 31 (2) (2009) 600–606, https://doi.
org/10.1016/j.engstruct.2008.11.010.
[210] M.H. Arslan, An evaluation of effective design parameters on earthquake
performance of RC buildings using neural networks, Eng. Struct. 32 (7) (2010)
1888–1898, https://doi.org/10.1016/j.engstruct.2010.03.010.
[211] K. Morfidis, K. Kostinakis, Approaches to the rapid seismic damage prediction of
r/c buildings using artificial neural networks, Eng. Struct. 165 (2018) 120–141,
https://doi.org/10.1016/j.engstruct.2018.03.028.
[212] K. Morfidis, K. Kostinakis, Comparative evaluation of MFP and RBF neural
networks’ ability for instant estimation of r/c buildings’ seismic damage level,
Eng. Struct. 197 (2019), 109436, https://doi.org/10.1016/j.
engstruct.2019.109436.
[213] B.K. Oh, B. Glisic, S.W. Park, H.S. Park, Neural network-based seismic response
prediction model for building structures using artificial earthquakes, J. Sound
Vib. 468 (2020), 115109, https://doi.org/10.1016/j.jsv.2019.115109.
[214] S.N. Somala, K. Karthikeyan, S. Mangalathu, Time period estimation of masonry
infilled RC frames using machine learning techniques, Structures 34 (2021)
1560–1566, https://doi.org/10.1016/j.istruc.2021.08.088.
[215] H.D. Nguyen, N.D. Dao, M. Shin, Prediction of seismic drift responses of planar
steel moment frames using artificial neural network and extreme gradient
boosting, Eng. Struct. 242 (2021), 112518, https://doi.org/10.1016/j.
engstruct.2021.112518.
[216] R. Falcone, A. Ciaramella, F. Carrabs, N. Strisciuglio, E. Martinelli, Artificial
neural network for technical feasibility prediction of seismic retrofitting in
existing RC structures, Structures 41 (2022) 1220–1234, https://doi.org/
10.1016/j.istruc.2022.05.008.
[217] D. Jia, Z. Wu, Structural probabilistic seismic risk analysis and damage prediction
based on artificial neural network, Structures 41 (2022) 982–996, https://doi.
org/10.1016/j.istruc.2022.05.056.
[218] C. Li, H. Li, X. Chen, Fast seismic response estimation of tall pier bridges based on
deep learning techniques, Eng. Struct. 266 (2022), 114566, https://doi.org/
10.1016/j.engstruct.2022.114566.
[219] P.C. Lazaridis, I.E. Kavvadias, K. Demertzis, L. Iliadis, L.K. Vasiliadis, Structural
damage prediction of a reinforced concrete frame under single and multiple
seismic events using machine learning algorithms, Appl. Sci. 12 (8) (2022) 3845,
https://doi.org/10.3390/app12083845.
[220] S. Hwang, S. Mangalathu, J. Shin, J. Jeon, Estimation of economic seismic loss of
steel moment-frame buildings using a machine learning algorithm, Eng. Struct.
254 (2022), 113877, https://doi.org/10.1016/j.engstruct.2022.113877.
[221] M. Salkhordeh, F. Alishahiha, M. Mirtaheri, S. Soroushian, A rapid neural
network-based demand estimation for generic buildings considering the effect of
soft/weak story, Struct. Infrastruct. Eng. (2022) 1–20, https://doi.org/10.1080/
15732479.2022.2081340.
[222] R. Haj-Ali, H. Kim, Nonlinear constitutive models for FRP composites using
artificial neural networks, Mech. Mater. 39 (12) (2007) 1035–1042, https://doi.
org/10.1016/j.mechmat.2007.05.004.
[223] M. Mozaffar, R. Bostanabad, W. Chen, K. Ehmann, J. Cao, M.A. Bessa, Deep
learning predicts path-dependent plasticity, Proc. Natl. Acad. Sci. 116 (52) (2019)
26414–26420.
[224] F. Tao, X. Liu, H. Du, W. Yu, Learning composite constitutive laws via coupling
Abaqus and deep neural network, Compos. Struct. 272 (2021), 114137, https://
doi.org/10.1016/j.compstruct.2021.114137.
[225] D.P. Jang, P. Fazily, J.W. Yoon, Machine learning-based constitutive model for
J2- plasticity, Int. J. Plast. 138 (2021), 102919, https://doi.org/10.1016/j.
ijplas.2020.102919.
21
C. Wang et al.
Journal of Industrial Information Integration 33 (2023) 100470
[234] B.K. Oh, J. Kim, Optimal architecture of a convolutional neural network to
estimate structural responses for safety evaluation of the structures, Measurement
177 (2021), 109313, https://doi.org/10.1016/j.measurement.2021.109313.
[235] E.A. Moscoso Alcantara, M.D. Bong, T. Saito, Structural Response Prediction for
Damage Identification Using Wavelet Spectra in Convolutional Neural Network,
Sensors 21 (20) (2021) 6795, https://doi.org/10.3390/s21206795.
[236] T. Li, Y. Pan, K. Tong, C.E. Ventura, C.W. de Silva, Attention-based sequence-tosequence learning for online structural response forecasting under seismic
excitation, IEEE Trans. Syst. Man Cybern Syst. 52 (4) (2021) 2184–2200, https://
doi.org/10.1109/TSMC.2020.3048696.
[237] Y. Xu, Y. Fei, Y. Huang, Y. Tian, X. Lu, Advanced corrective training strategy for
surrogating complex hysteretic behavior, Structures 41 (2022) 1792–1803,
https://doi.org/10.1016/j.istruc.2022.05.097.
[238] B. Ahmed, S. Mangalathu, J. Jeon, Seismic damage state predictions of reinforced
concrete structures using stacked long short-term memory neural networks,
J. Build. Eng. 46 (2022), 103737, https://doi.org/10.1016/j.jobe.2021.103737.
[239] H. El Kadi, Predicting the crushing behavior of axially loaded elliptical composite
tubes using artificial neural networks, Appl. Compos. Mater. 15 (4) (2008)
273–285, https://doi.org/10.1007/s10443-008-9074-2.
[240] T.G. Wakjira, A. Rahmzadeh, M.S. Alam, R. Tremblay, Explainable machine
learning based efficient prediction tool for lateral cyclic response of posttensioned base rocking steel bridge piers, Structures 44 (2022) 947–964, https://
doi.org/10.1016/j.istruc.2022.08.023.
[241] Y. Hu, W. Guo, Y. Long, S. Li, Z. Xu, Physics-informed deep neural networks for
simulating S-shaped steel dampers, Comput. Struct. 267 (2022), 106798, https://
doi.org/10.1016/j.compstruc.2022.106798.
[226] A. Malik, M. Abendroth, G. Hütter, B. Kiefer, A Hybrid approach employing
neural networks to simulate the elasto− plastic deformation behavior of 3D-foam
structures, Adv. Eng. Mater. 24 (2) (2022), 2100641, https://doi.org/10.1002/
adem.202100641.
[227] F. Tao, X. Liu, H. Du, W. Yu, Finite element coupled positive definite deep neural
networks mechanics system for constitutive modeling of composites, Comput.
Methods Appl. Mech. Eng. 391 (2022), 114548, https://doi.org/10.1016/j.
cma.2021.114548.
[228] R. Guarize, N. Matos, L. Sagrilo, E. Lima, Neural networks in the dynamic
response analysis of slender marine structures, Appl. Ocean Res. 29 (4) (2007)
191–198, https://doi.org/10.1016/j.apor.2008.01.002.
[229] R. Zhang, Z. Chen, S. Chen, J. Zheng, O. Büyüköztürk, H. Sun, Deep long shortterm memory networks for nonlinear structural seismic response prediction,
Comput. Struct. 220 (2019) 55–68, https://doi.org/10.1016/j.
compstruc.2019.05.006.
[230] B.K. Oh, B. Glisic, Y. Kim, H.S. Park, Convolutional neural network-based windinduced response estimation model for tall buildings, Comput. Aided Civ.
Infrastruct. Eng. 34 (10) (2019) 843–858, https://doi.org/10.1111/mice.12476.
[231] R. Zhang, Y. Liu, H. Sun, Physics-informed multi-LSTM networks for
metamodeling of nonlinear structures, Comput. Methods Appl. Mech. Eng. 369
(2020), 113226, https://doi.org/10.1016/j.cma.2020.113226.
[232] D. Thaler, M. Stoffel, B. Markert, F. Bamer, Machine-learning-enhanced tail end
prediction of structural response statistics in earthquake engineering, Earthq. Eng.
Struct. Dyn. 50 (8) (2021) 2098–2114, https://doi.org/10.1002/eqe.3432.
[233] J. Xue, Z. Xiang, G. Ou, Predicting single freestanding transmission tower time
history response during complex wind input through a convolutional neural
network based surrogate model, Eng. Struct. 233 (2021), 111859, https://doi.
org/10.1016/j.engstruct.2021.111859.
22