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A Proposed Plan for a Reliable and Effective Scheme for Corrosion Monitoring
in Reinforced Concrete Structures Utilizing a Distributed Sensing
Conference Paper · July 2014
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A Proposed Plan for a Reliable and Effective Scheme for Corrosion
Monitoring in Reinforced Concrete Structures Utilizing a
Distributed Sensing*
First, Ying Zhao, Second, Mohammad Noori, Third, Haifeng Wang,
Forth, Andrea Galtarossa, Fifth, An Sun, and Sixth, Hui Jin
1
Abstract— Reinforced concrete (RC) structures are the most common types of structures in China and one of the most popular
structures utilized in civil infrastructure systems, in the rapidly growing urban development in China. In general reinforced
concrete construction is relatively cost-effective, and these structures are fire resistant, have long service life and high structural
integrity. However, a major issue facing RC structures, especially in China, is the corrosion of reinforcing steel bars, due to harsh
and corrosive environment in most urban areas. The cost associated with the repair of corroded reinforcing bars, or the
rehabilitation of the corroded reinforced component, is very high, especially that the internal damage, due to corrosion, cannot be
easily detected and the damage is not restricted to specific locations in the structure, such as joints, and it may occur at any location
along the reinforcing bars. This requires a continuous and distributed sensing and detection of corrosion along rebars. Major
factors that cause the deterioration of reinforced concrete structures due to corrosion are chloride ingress, in the case of marine
environment, and carbonation of concrete in the case of urban environment. Although advanced non-destructive testing (NDT)
techniques have been studied and over the past two decades several methodologies have been developed for the detection of
corrosion in RC structures, a reliable approach that can accurately and reliably identify the location of the corrosion damage inside
RC structures is still lacking. In this paper, an extensive review of literature dealing with the corrosion monitoring in reinforced
concrete structures is presented and a plan for a proposed research on a newly developed active, distributed, real-time structural
health monitoring system (SHMS) integrated with a diagnostic measurement system that incorporates the appropriate and
advantageous features of several schemes such as wavelets, artificial neural networks, and support vector machines is discussed. It
is expected that in this new research, the dynamically quantified damage parameters are recorded and analyzed efficiently and
effectively, via an integrated distributed sensing and in conjunction with the aforementioned integrated signal processing technique.
The proposed experimental-analytical approach is expected to show promises for the identification and prediction of corrosion both
in terms of the size, severity, and location via utilizing a newly developed fiber optic based distributed sensing system.
INTRODUCTION
With a growing concern on the premature deterioration of civil infrastructure systems, in the rapidly developing urban
systems in China, the demand for the development of strategies and new technologies for quantifying the structure’s health
condition is ever increasing. A large number of infrastructure systems in China, are made of reinforced concrete and the leading
cause of degradation of RC structures is corrosion damage to the reinforcement bar (rebar) embedded in the concrete, which is a
costly problem.
According to a latest report published in 2013 in the United States, the Department of Defense (DOD) reported spending
an estimated $20.8 billion annually to prevent and mitigate corrosion of its assets, including military equipment, weapons, and
facilities and other infrastructure. While the vast majority of these costs are related to corrosion issues on military equipment
and weapons, the cost of corrosion at DOD facilities and other infrastructure was estimated to be about $1.9 billion annually [1].
The National Aeronautics and Space Administration (NASA) engineers and scientists constantly concern themselves with
the challenges humans face living in the harsh environment of space. The harsh environmental effects of life on Earth are the
focus of a group at NASA's Kennedy Space Center in Florida. The spaceport's location near the Atlantic Ocean presents
numerous opportunities to both study and attempt to solve the destructive effects of corrosion. "Kennedy Space Center has the
most aggressive corrosion environment among all government facilities for which data is available except for ships at sea," [2]
explained Luz Marina Calle, founder and technical lead for the space center's Corrosion Technology Laboratory.
It was clearly elaborated in a report in 2004 that, at US $2.2 trillion, the annual cost of corrosion worldwide is over 3% of
the world’s GDP [3]. A study by the US Transportation Research Board (TRB) in 2006 indicated that the “Corrosion-induced
deterioration of reinforced concrete bridge superstructure elements is a common and recurring problem in the United States.”
[4]The TRB estimated that deicing, sea salts and other corrosive sources have caused $150 billion worth of corrosion damage to
interstate highway bridges. Also, according to a recent study by Lizhegli Peng and Mark Stewart, published on 16 January
2014, “a changing climate which leads to increases in atmospheric CO2 concentration, and changes in temperature and relative
humidity (RH), especially in the longer term, will accelerate the deterioration processes and consequently decline the safety,
serviceability and durability of reinforced concrete (RC) infrastructure in China.” [5]
* Resrach supported by the International Institute for Urban Systems Engineering, IIUSE, Southeast University, Nanjing, China, a grant provided by 1000
Program for the Recruitment of Global Experts by the Chinese Government, and a grant by Shuangchuang Program, Jiangsu Province, China.
F. Y. Zhao is a doctoral research assistant, with IIUSE, Southeast University, Nanjing, 210096, China (email: yzhseu@gmail.com).
S. M. Noori is an Affiliated Professor with IIUSE, and a Professor at California Polytechnic State University, San Luis Obispo, California, 93407, USA
(email: mohammad.noori@gmail.com).
T. H. Wang is a master research assistant at IIUSE, Southeast University, Nanjing, 210096, China (email: wang19891218@126.com).
F. A. Galtarossa is a professor at University of Padova, Padova, Italy (email: andrea.galtarossa@dei.unipd.it).
F. A. Sun is an Associate Professor at IIUSE, Southeast University, Nanjing, 210096, China (email: ansun_iiuse@163.com).
S. H. Jin is an Associate Professor with IIUSE, Southeast University, Nanjing, 210096, China (email: jinhui1018@gmail.com).
The World Corrosion Organization (WCO), whose founding members are Australasian Corrosion Association (ACA),
European Federation of Corrosion (EFC) and Chinese Society for Corrosion and Protection (CSCP), plays a major role in
ensuring that governments, industry, academia, and the general public understand that by following appropriate strategies and
obtaining sufficient resources for corrosion programs, the best engineering practices can be achieved. The main obligation of
WCO, clearly illuminated in the White Paper, is to satisfy 6.3 billion people on this globe, including proper nutrition, clean
water, good health, safe housing, dependable energy, effective communication and mobility, for the purposes of raising public
awareness of corrosion and corrosion control, identifying world best practices in corrosion management, facilitating the
provision of corrosion control expertise to governments, industries, and communities and normalizing corrosion-related
standards worldwide [6].
Corrosion is a common but probably overlooked problem, involving almost every walk of life. Premature deterioration of
structures has evolved during the past four decades to become a formidable global technological and economic problem, as a
consequence of the ingress of chloride or carbon dioxide, leading to accelerated corrosion of reinforcing steel bars or
carbonation of concrete respectively. Fig.1 shows a distribution of airborne salinity worldwide for the summer climatic situation
combined with both continental and ocean conditions in the coastal regions, indicating severe corrosion potential. The regions
with high continental airborne salinity are marked in red on the map like the sites with high airborne salinity in the ocean [7].
Fig.2 indicates a rough distribution condition of global carbon dioxide emissions, which is also an important factor triggering
corrosion [8].
Figure 1. Airborne salinity classification including the
wind condition for the summer climatic situation
Figure 2. Distribution condition of global
carbon dioxide emissions
CORROSION MECHANISMS
Corrosion of reinforcement has been established as the predominant factor causing widespread premature deterioration of
concrete construction worldwide, especially for the structures located in the coastal marine environment and highly polluted
urban areas resulted from carbon dioxide emission. Fig. 3 indicates the relationship between corrosion and environment,
materials, components and structures.
Figure 3. Diagram showing the relationship between corrosion
and environment, materials, components and structures
Corrosion in reinforced concrete structures is broadly regarded as a durability problem. In general, the high alkalinity (pH
over 12.5) of the concrete pore water in civil structures brings about a passive layer forming on the steel bars in initial phase,
which protects steel bars from the risk of corrosion. The protection layer may be destroyed when it occurs to the two common
cases: localized destruction of the passive film on the steel bars by chloride ions, resulting in pits, randomly distributed along
the steel bars; and general breakdown of passivity by neutralization of the concrete, leading to early cracking and spalling of the
concrete, often with comparative reduction of the cross section of the steel bars.
There are mainly four negative effects of chloride-containing concrete on both concrete and steel bars, especially in the
alternate wetting and drying environment. The chloride ions destroy the passive film of the steel bars, reduce the pH of the pore
water, increase the moisture content and increase the electrical conductivity of concrete. At the same time, the carbonation of
concrete spontaneously proceeds as the internal condition and external environment change. The processes of carbonation of
concrete form the carbonic acid by taking up CO2 dissolved in the pore water, dissolve the alkaline constituents and produce
carbonate-bicarbonate equilibrium by taking in water within concrete.
The transport of gases (O2, CO2), water and ions (chloride, Cl¯) in concrete occurs possibly through crevices, gravel and
the pore system, which is mainly caused by the following four basic mechanics, capillary suction, due to capillary action inside
capillaries of cement paste, permeation (gases, water), due to pressure gradients, diffusion (ions), due to concentration
gradients, and migration, due to electrical potential gradients [9-11].
Generally, corrosion that causes structural damage goes through several phases as shown in Fig. 4. Corrosion initially
induced by chloride or pH lower than 11.5 causes localized attack on steel bars within concrete, even they might remain in
passive state as non-corroding parts, where the corrosion current of the macrocell develops, i.e. a galvanic element between an
anode and a cathode, which refer to the area of localized corrosion attack and non-corroding area respectively. During the
electrochemical anode reaction, the metal loses electrons and its mass decreases, while the products of metal corrosion
accumulate around the cathode. The deterioration of reinforced concrete structures result from the fact that the corrosion
products formed around rusted reinforcements are more voluminous than the corroded steel, which gives rise to radial stress
that distorts the neighboring concrete under pulling stress. Consequently, cracks arise in the concrete and its cover spalls,
therefore, the bond performance between rebars and concrete decreases, even triggering rebars uncovered and decrease of
structural stiffness and bearing capacity [12-14]. Fig. 5 shows the relationship between steel bars, concrete and their interactive
bonding.
Figure 5. Bonding relationship between steel bars and concrete
Figure 4. Four phases of corrosion development
STRUCTURAL HEALTH MONITORING SYSTEM
The essence of Structural Health Monitoring (SHM) has been clearly reflected, where it is defined as the continuous or
regular measurement and analysis of key structural and environmental parameters under operating conditions, for the purpose of
warning of abnormal states or accidents at an early stage [15]. The entire process of monitoring, diagnostics and evaluation of a
structural system is indicated in Fig 6.
In general, the SHMS comprises of four major parts: sensing technology, diagnostic signal generation, signal processing,
damage identification and interpretation, and integration. Recent advances in sensing technology, smart materials such as
piezoelectric materials and fiber optics telecommunication technology, with current developments in computations have
resulted in a significant interest in investigating the economic, engineering, operational, and logistical benefits associated with
developing SHMS technologies that can be integrated into structures as an active, built in diagnosis system [16-19, 116-118].
Corrosion damage reduces the service life of structures and can create serious safety hazards. Conventional techniques for
detection and assessing the health of a reinforced concrete structure — such as half-cell potentials [20-21], surface potentials of
concrete [22-23], concrete resistivity [24-25], linear polarization resistance [26-27], Galvanostatic pulse transient [28],
electrochemical impedance spectroscopy [29-31], harmonic analysis [32-33] and noise analysis [34-35]—involve considerable
uncertainty and randomness.
Fiber optical sensing technique has been widely used for its unique superiority over other techniques. Generally, fiber
optical sensors can be classified as follows according to the spatial distribution of the measurand [36-40].
•
Point sensors: the measurement is carried out at a single point in space, but possibly multiple channels for addressing
multiple points. (e.g. Fabry-Perot sensors and single Fiber Bragg Grating (FBG) sensors).
•
Integrated sensors: the measurement averages a physical parameter over a certain spatial section and provides a single
value. (e.g. deformation sensor measuring strain over a long base length).
•
Quasi-distributed or multiplexed sensors: the measurand is determined at a number of fixed, discrete points along a
single fiber optical cable. (e.g. multiplexed FBG's).
•
Distributed sensor: the parameter of interest is measured with a certain spatial resolution at any point along a single
optical cable. (e.g. systems based on Rayleigh, Raman and Brillouin).
Figure 6. Monitoring, diagnostics and evaluation of a structural system
Both conventional and modern techniques, either electrochemical or nondestructive testing (NDT), are widely available for
corrosion damage detection, analysis and maintenance of equipment. Some typical commercial corrosion equipment such as
CorrviewTM [41], SmartPebbleTM [42] have also been researched and developed.
Several types of signal processing techniques that have been utilized in aerospace, geophysics, and medical diagnosis
fields for NDT have been applied in NDT for concrete structures, such as ultrasonic testing (UT) [43-47], redar (Microwave),
radiographic (Neutron, X-ray, Gamma) testing (RT) [48-51], electromagnetic testing [52-53] and acoustic emission (AE or
AET) [54-56]. Moreover, in recent years, more advanced diagnostic techniques have been developed and have demonstrated
promising results for integration with sensing technologies for monitoring and detection of damage and structural health.
Furthermore, alternatives for steel reinforcement including fiber reinforced polymers have offered promising possibilities for
mitigating damages such as corrosion. Implementation of these new diagnostic methods [57-72] and technologies in health
monitoring of concrete structures offers many challenges and requires additional development due to high inhomogeneity of
concrete structures.
Damage identification and interpretation algorithms are considered as the "brain" of the SHMS. The interpretation
algorithms, which are dependent on the physical diagnostic signal category, cover a wide variety of techniques from
conventional methods like optimization to neural networks [73-74]. Despite the fact that these diagnostic algorithms have
shown promise, their implementation for damage detection in concrete structures, in combination with the right sensing
technology has not been addressed in the literature.
Although outward indications of structural damage, such as pitting and spalling, are the first indications that corrosion has
initiated, the structure may be near failure and require immediate replacement by that time. Despite extensive research in this
area over the past decade and even incorporation of multi-functional and FBG sensors, a reliable and robust system that can
monitor and identify the timely initiation and progression of corrosion throughout the entire length of a rebar, rather than only at
selected points, is still lacking. Fig. 7 reflects the trend of the development of structural health monitoring systems. Therefore,
an integrated and cost-effective sensor system, that can be permanently embedded in a concrete structures to monitor the
development of corrosion process both on new or existing reinforced concrete structures, is not only important but also
beneficial to assure the longevity of critical infrastructure systems.
Figure 7. Trend of the development of structural health monitoring system
Given the state of the art of research and the shortcomings in this important area, the fact that the location of the
corrosion in reinforced concrete structures has a high level of randomness and uncertainty, the main goal of this research is to
develop a novel, highly effective, and reliable approach that can monitor and detect corrosion in the entire structure at any
point as it occurs. In order to achieve this, the main objectives of our research will include:
• To identify and utilize the most effective, practical and reliable corrosion monitoring mechanism that can identify the
physically strain based changes in a rebar due to corrosion, such as change in geometry.
•
To identify, develop and utilize a highly effective, economical, and reliable, high-resolution, fully distributed sensing
system and bendable sensor, and address optimization of optical fiber sensing system, cross sensitivity problems and
multi point techniques of a sensing system.
•
To develop a highly effective signal processing software based on integration and utilization of diagnostic damage
detection techniques, such as wavelets, artificial neural network, least square support vector machines, combined with
Particle Swarm Harmony Search Algorithm and other promising methods that are not model dependent, and have
shown promising results for detection of the location, size and occurrence time of damage in a noisy environment.
•
To develop a series of studies on quantification of damage due to corrosion and the correlation with the overall
structural health performance and degradation, or in other words the effect of corrosion on the overall safety of the
structure.
The proposed work builds on earlier extensive damage detection diagnostic work [75-103] by Professor Mohammad
Noori and his team and in collaboration with Professor Andrea Galtarossa, an experienced and highly knowledgeable
researcher in the field of fiber optic sensing, as well as a group of doctoral and MS students, a post-doc and other members of
the research staff at the International Institute of Urban Systems Engineering (IIUSE), at Southeast University. The main goal
of this study is to build a novel monitoring system for corrosion detection in reinforced concrete structures that will address
the four objectives described above. This research will primarily build upon:
• An extensive prior research experience of the team on the development of wavelet analysis, artificial neural networks
and support vector machines utilizing harmony search algorithms, and incorporating these techniques, that have been
successfully developed and applied by this team for the time and location identification of damage in a variety of
structures including nonlinear systems.
•
Integrating these tools with an Optical Backscatter Reflectometer™ (OBR) distributed optical fiber sensing technology
that is the latest distributed sensing technology with the capability to “see” any changes in a fiber assembly or network
out to 500 meters with no dead-zone and with a millimeter resolution.
•
Comparison of existing methods, advantages and disadvantages as well as applying different techniques and comparing
OBR vs FBG to better understand the correlation between corrosion and the strain (change in rebar geometry) and how
best it can be measured and used as a reliable parameter for corrosion detection.
The novelty of this research, which will be the phase one of an extensive and fundamental work for corrosion damage
detection and management in RC Structures, is the detection of corrosion at any point (high resolution, continuous monitoring
in contrast to point detection) of a long rebar. The feasibility of this new sensing system versus other advanced techniques,
such as FBG sensors for point sensing, will also be investigated and experimental research that will replicate a wide range of
corrosive environments induced by various mechanisms, will be conducted in order to assure the validity, reliability, and
sustainability of the proposed novel approach. Given the extensive experimental study for verification and feasibility, and to
deeply understand the corrosion detection at any point along a rebar, the study in this phase will be focused on detection of the
presence, and quantification of the size and location, of corrosion damage in real time for rebars only, and not embedded in
concrete. Therefore, this phase of research, briefly elaborated herein, will identify and address a basic scientific and
engineering challenge toward establishing SHM in RC structures. The proposed research will lay the groundwork for the
future use of a distributed and highly efficient, low power and durable sensor network for damage identification and
continuous monitoring using a robust integrated diagnostic sensor-data analysis technique. It is envisioned that the proposed
work will significantly advance scientific knowledge in the areas of sensor technology for detection of corrosion in RC
structures, innovative integrated analytical/signal processing techniques, damage reconstruction and visualization, and
laboratory methodologies for corrosion identification. Developed knowledge and methodologies will be integrated into
education via development of relevant courses and professional workshops at the International Institute for Urban Systems
Engineering and will lay the foundation for a broader impact on identification of corrosion in other fields and other types of
critical and life-line structures. Also, through involving undergraduate students from the new Honors College at Southeast
University, in the experimental work in the laboratory testing and analysis more quality Chinese graduate students will be
recruited in the future to carry out doctoral studies. Moreover, through collaborations between IIUSE and the research
laboratory of Professor Andrea Galtarossa, in the Department of Information Engineering (DEI), University of Padova,
experimental and other aspects of this research will lead to promoting multidisciplinary and global research collaborations. In
the following, some highlights of the various steps and aspects of the aforementioned research plan are presented.
STATE-OF-THE-ART DISTRIBUTED SENSING TECHNIQUE
Distributed sensing techniques have gone through several periods of major advancements over the past two decades, i.e.
distributed sensing by Optical Frequency Domain Reflectometer (OFDR) developed at NASA in 1996 by Mark Froggatt,
currently Luna CTO, commercialization of distributed temperature and strain sensing using OFDR and Fiber Bragg Grating
(FBG) fiber in 2004, Commercial release of Rayleigh scatter-based sensing system as extension of Luna’s award-winning
Optical Backscatter ReflectometerTM (OBR) in 2006 and Launch of dedicated distributed sensing system in 2011.
A. Backscattering Radiation
The basic underlying physical processes for achieving a distributed sensor, as shown in Fig. 8, are provided by various
scattering processes. As the laser light is propagating along the optical fiber, a small amount of light is continuously scattered
back at each location along the fiber, including Rayleigh, Brillouin, and Raman peaks or bands [104].
Rayleigh scattering is due to reflections at random inhomogeneity of the refractive index frozen in during manufacture of
the fiber. The Rayleigh component controls the main slope of the decaying intensity curve and may be used to identify the
breaks and heterogeneities along the fiber. Raman scattering is due to the interaction with molecular vibrations and rotations in
the glass. The intensity of the anti-Stokes component of the Raman radiation increases with temperature, while the Stokes
component remains stable; the power ratio between these two peaks together with the time of the arrival provides information
about the local temperature and position, respectively. Brillouin scattering is due to the interaction with inhomogeneity created
by sound waves in the fiber (acoustic phonons). Brillouin-based techniques are consequently, inherently more accurate and
more stable on the long term since intensity-based techniques suffer from a higher sensitivity to drifts.
Figure 8. Wavelengths of the backsattered radiation
Brillouin optical time-domain reflectometer (BOTDR) resolves the strain or temperature based on Brillouin scattering of
a single pulse. Brillouin optical time-domain analysis (BOTDA) uses a more complicated phenomenon known as stimulated
Brillouin scatter (SBS). The classical BOTDR is essentially an OTDR with a strong filter to avoid the Rayleigh radiation and
with an additional device to discriminate the wavelength of the Brillouin peak. Consequently, it will have the same advantages
and limitations of OTDRs: very long interrogation distances and low spatial resolution.
B. Optical Backscatter Reflectometer
The Optical Backscatter Reflectometer uses swept wavelength interferometry (SWI) to measure the Rayleigh backscatter
as a function of length in the optical fiber with high spatial resolution. An external stimulus (like a strain or temperature change)
causes temporal and spectral shifts in the local Rayleigh backscatter pattern. These temporal and spectral shifts can be measured
and scaled to give a distributed temperature or strain measurement. The SWI approach enables robust and practical distributed
temperature and strain measurements in standard fiber (single connection but multiple sensors) with millimeter scale spatial
resolution over tens to hundreds of meters of fiber with strain and temperature resolution as fine as 1 microstrain and 0.1°C.
This technique can measure complex strain profile vs. time & position, rather than just strain at a single point vs. time. The
sensing length can be up to 70 meter, and provides more than 200 sensing points per meter of fiber with a single connection.
This allows continuous monitoring of rebars in contrast to point sensing that has been a shortcoming in the past requiring a large
number of fiber optic sensors [105,118-119].
The 3D image shown in Fig. 9 clearly shows a cantilever beam’s high spatial parametric relationship between strain and
position with the time changing. Regions of distributed sensing, where the spatial resolutions are as millimeter scale, can be
defined, set and observed as well in software, indicating the scatter is recorded in each, small, discrete segment of the fiber, as
shown in Fig. 10.
Figure 10. Scatter recorded in segments of fiber
Figure 9. Spatial parametric relationship of a cantilever
Point measurement such as strain gages or fiber Bragg grating are available, but the information about the response to
loads is restricted only to those points on which the sensors were bonded. Unless a sensor is located near the damage initiation
point, details about the failure initiation and growth are lost. With the newly developed distributed OBR system, the information
is given as an array of data with the position in the optical fiber and the strain or temperature data at this point. This technique
shows a promising application value in monitoring and quantification of highly uncertain and random corrosion behavior all
along the steel bars in concrete structures with high spatial resolution.
The new distributed sensing technique may significantly change the traditional way of local detection, on account of its
promising advantage of a full coverage of the instrumented area for distributed monitoring for structures. Table I shows a rough
comparison of some main performance parameters between the following techniques, FBG, BOTDA, BOTDR and OBR.
TABLE I.
COMPARISON OF TECHNICAL PARAMETERS
FBG
BOTDA
BOTDR
OBR
Strain accuracy
Spatial resolution
Temperature
accuracy
Length range
±1µε
Related to grating length
±2µε
0.1m
±30µε
0.1m
±1µε
0.001m
±0.1°C
±0.1°C
N.A.
±0.1°C
Point sensors
100km
100km
Acquisition time
Typically 3kHz
1second
0-20minutes
70-100m
Related to accuracy and
length
MicronOptics, Fibersensing,
Insensys
OZ Optics, Omnisens,
Neubrex
Yokogawa, Sensornet,
NTT
Sellers
LUNA
CORROSION DAMAGE ANALYSIS
The integrity of civil infrastructures needs to be inspected to determine the physical condition of the structures. Steel
reinforced concrete (RC) is widely used in civil infrastructures such as buildings, bridges, and highway systems. Civil
infrastructures are usually large in physical size and complex compared to any other structures, and in many areas in China are
exposed to harsh environments at all times, and are susceptible to natural disasters such as earthquakes, hurricanes, etc..
Therefore, the maintenance and damage inspection of these structures can be costly and time consuming. Any downtime could
cause a significant economic impact on the society. For critical structures, such as high rise buildings, hospitals, bridges and
power stations, it is imperative that their health be assessed in real time for a major catastrophic event. In many cases, the
impending collapse of a structure may not even be visible from the exterior.
The modeling for the corrosion processes of steel bars can be regarded as an electrochemical process, where corrosion
reaction equation, including chloride diffusion and carbonation of concrete, can be built to simulate the influence of corrosion
when it occurs. The other way is establishing a temperature varied layer, as an expansion ring, between the steel bars and
concrete. Based on the two approaches described above, the mechanism of corrosion that gives rise to the debonding between
steel bars and concrete as well as concrete crack could be clearly simulated. Some important factors, such as concentration of
chloride, CO2 and O2, concrete compactness, thickness of concrete protection layer, steel bars spacing, numbers of steel bars,
are directly related to the damages caused by corrosion.
It is of great importance to consider the decrease of bonding performance between steel bars and concrete and stiffness
reduction to the structures. Based on the modeling concept of cracking shear transfer behavior, an open-slip coupled interface
model is developed to simulate both pull out tests and axial tension tests. On the basis of different levels of corrosion damage
for components at different locations, the reliability and durability of reinforced concrete structures are carried on to contrast
between corroded and non-corroded structures, for both numerically simulated static and dynamic models, compared with a
reinforced concrete prototype built, through accelerated corrosion experiment monitored via utilizing the new OBR system.
STRUCTURAL MODEL ESTABLISHMENT
Generally, system identification techniques can be categorized as being either parametric or nonparametric. Parametric
identification techniques seek to determine the “optimal parameters” of an assumed model structure such that the modeled
response closely matches the recorded system response, while nonparametric techniques attempt to identify the system’s
“optimal functional representation” without any priori assumptions with respect to the model’s structure. Numerous
researchers have studied the system identification [106-115].
Figure 11. Structural monitoring and diagnostics system
For a dynamic model characterized by non-linearity and time-varying, the inherent limitation for the parametric
identification technique may not be quite challenging in case of non-linear and hysteretic systems, and nonparametric
identification technique provides little information with regard to the structure’s dynamic parameters. The tentative work we are
going to do is to combine the advantages of parametric and non-parametric techniques integrated with artificial neural networks,
i.e. the model structure is determined by the first principles and the model parameters are estimated from the measured data.
DIAGNOSTICS AND ASSESSMENT
A wavelet-based damage identification method will be proposed and integrated, which includes four phases, i.e. damage
alarming, damage validity, damage location and damage qualification (Fig. 11). Damage and noise of different standards, and
the properties of dynamic parameters are investigated when damage occurs in the dynamic system. By using wavelet transform,
differential equation of the structural dynamic system is decomposed and dynamic parameters are described based on different
scales.
In order to extract damage features from noisy signal, mufti-scale analysis and orthogonality of wavelet packet transform
are investigated to reveal different packet energy distributions, on account that for a special kind of damage the wavelet packet
energy distribution is different at different measurement nodes. Damage features extracted from structural response signal can
be distinctly clarified with the wavelet packet energy distribution which is robust to noise.
Data is obtained from the aforementioned novel and intelligent dynamic system. Artificial neural networks (ANNs) and
support vector machines (SVMs) are used as pattern classifiers to make a system decision for different kinds of damage patterns
and implement multi-damage recognition, damage localization and differentiate the degree of damage. Various design methods
of neural networks and combination of different kernel functions of support vector machines are discussed, and accuracy,
efficiency of algorithms and applicability of ANNs or SVMs are carried out to be revised and improved.
CONCLUSION
Structural health control and monitoring system is to analyze online behavior of a structure accurately and efficiently, to
assess its performance under various service loads, to detect damage or deterioration, and to determine the health or condition of
the structure. Corrosion has been a crucial defect in structural systems, which may lead to catastrophic results if neglected.
World Corrosion Organization is committed to working on a common goal of sparing no effort to use wisdom of human beings,
making full use of resources from nature, thus reducing the impact of people’s daily life under corrosion.
In this paper, varieties of NDT techniques have been discussed to explore the corrosion mechanism and evaluate structural
health condition and its reliability. Addressing these challenges will demand an integrated body of scientific and engineering
research targeted at specific needs but coordinated to minimize duplication of effort and to take advantage of synergism. The
internationally leading sensing technique OBRTM uses swept wavelength interferometry (SWI) to measure the Rayleigh
backscatter as a function of length in the optical fiber with high spatial resolution. A series of the analytical-experimental
approaches and related data diagnostics methods, with respect to corrosion problems that occurrs in reinforced concrete
structures in China are also discussed and will be explored in the proposed research.
The proposed work builds on earlier extensive work by the research team build a novel monitoring system for corrosion
detection in reinforced concrete structures. The main objectives of this proposal are:
•
To identify and utilize the most effective, practical and reliable corrosion monitoring mechanism that can identify the
physical changes in a rebar due to corrosion.
•
To identify, develop and utilize a highly effective, economical, and reliable high spatial resolution based fully
distributed sensing system.
•
To develop a series of studies on quantification of damage due to corrosion and the correlation with the overall
structural health performance and degradation.
•
To develop a highly effective signal processing software based on integration and utilization of diagnostic damage
detection techniques, such as wavelets, artificial neural network, least square support vector machines.
Developed knowledge and methodologies will be integrated into education at International Institute for Urban Systems
Engineering (IIUSE), Southeast University, via development of relevant courses and professional workshops to promote
global strategic cooperation.
APPENDIX
A. Wavelet Theory
Wavelet transforms can be considered as forms of time-frequency representation for continuous-time (analog) signals and
so are related to harmonic analysis. Almost all practically useful discrete wavelet transforms use discrete-time filterbanks.
These filter banks are called the wavelet and scaling coefficients in wavelets nomenclature. These filterbanks may contain
either finite impulse response (FIR) or infinite impulse response (IIR) filters. The wavelets forming a continuous wavelet
transform are subject to the uncertainty principle of Fourier analysis respective sampling theory: Given a signal with some event
in it, one cannot assign simultaneously an exact time and frequency response scale to that event. The product of the
uncertainties of time and frequency response scale has a lower bound. Thus, in the scaleogram of a continuous wavelet
transform of this signal, such an event marks an entire region in the time-scale plane, instead of just one point. Also, discrete
wavelet bases may be considered in the context of other forms of the uncertainty principle.
•
Continuous wavelets transform (CWT)
The CWT is defined as
!
𝑊 𝑎, 𝑏 =
𝑓 𝑡 𝜓 ∗ !!!
𝑑𝑡
(1)
!
!
∗
Where a and b are scale and translation parameters, respectively and 𝜓 is the complex conjugate of 𝜓. The basis function
𝜓 is represented as
!
𝜓!,! (𝑡) = 2! 𝜓(2 ! 𝑡 − 𝑘)
(2)
If the scaling parameter a is 0<a≪1, it results in very narrow windows and is appropriate for high frequency components in
the signal 𝑓 𝑡 . If the value of a satisfies a≫1, it results in the very wide windows and is suitable for the low frequency
components in the signal. According to the uncertainty principle known as Heisenberg inequality, the resolution in time and
frequency has the following relationship:
!
∆𝑡 ∙ ∆𝑓 ≥
!!
Where ∆𝑓 is proportional to the center frequency f, which leads to
∆!
!
(3) = 𝐶 (4) Where C is a constant. Therefore, the time resolution becomes arbitrarily good at high frequencies, while the frequency
resolution becomes arbitrarily good at low frequencies. This property helps to overcome the limitation of Short Time Fourier
Transforms (STFT) in which the time-frequency resolution is fixed. In order for an inverse wavelet transform to exist, the
mother wavelet (basis function) should satisfy the admissibility condition
! |!(!)|!
𝑑𝜔
!! |!|
< ∞ (5) Where 𝛹 is the Fourier transform of 𝜓. Eq. (1) can be represented as
∗
𝑊 𝑎, 𝑏 = 𝑓 𝑡 , 𝜓!,!
(𝑡) (6) Therefore, CWT is a collection of inner products of a signal 𝑓 𝑡 and the translated and dilated wavelets 𝜓!,! (𝑡).
•
Discrete wavelet transform (DWT)
The main idea of DWT is the same as that of CWT. While the CWT requires much calculation effort to find the
coefficients at every single value of the scale parameter, generally DWT adopts dyadic scales and translations, i.e. scales and
translations based on powers of two, in order to reduce the amount of computation, which results in better efficiency of
calculation. Filters of different cutoff frequencies are used for the analysis of the signal at different scales. The signal is passed
through a series of high-pass filters to analyze the high frequencies, and through a series of low-pass filters to analyze the low
frequencies. In DWT the signals can be represented by details and approximations. The detail at level j is defined as
𝐷! = !∈! 𝑎!,! 𝜓!,! (𝑡)
Where Z is the set of positive integers. The approximation at level J is defined as
(7)
𝐴! = !!! 𝐷!
Finally, the signal 𝑓 𝑡 can be represented by
(8) 𝑓 𝑡 = 𝐴! + !!! 𝐷! (9) As opposed to the CWT where only a wavelet function is used, in DWT a scaling function is used, in addition to the
wavelet function. These are related to low-pass and high-pass filters, respectively. The scaling function can be represented as
∅ 𝑛 =
!!!
!!! 𝑐!
∅ 2𝑛 − 𝑗
(10) !
!
∅!,! (𝑡) = 2 ∅(2 ! 𝑡 − 𝑘) (11) Not all wavelet functions have scaling functions. Only orthogonal wavelets have their scaling functions. This DWT can be
very useful for on-line health monitoring of structures, since it can efficiently detect the time of a frequency change caused by
stiffness degradation.
B. Artificial Neural Network
Artificial neural networks are capable of realizing a variety of learning, approximate reasoning, generalization, noise
filtering, parallel processing, distributed knowledge base, non-linear relationships of considerable complexity and fault
tolerance.
Artificial neural networks are intelligent arithmetic computing elements which can represent, by learning from examples,
complex functions with continuous-valued as well as discrete outputs. These networks imitate the learning process in the brain,
and can be thought of as mathematical models for the operation of the brain in the way neurons process information. The
network as a whole corresponds to the collection of interconnected neurons. Each link has a numeric weight associated with it,
which is the primary means of long-term storage in neural networks, and learning usually takes place by updating these weights.
The weights are adjusted during a training process so as to bring the network’s input/output behavior more in line with the
derived phenomena being modeled by the network. Each node has a set of input and output links, a current activation level, and
a means of computing the activation level at the next step, given its inputs and weights. The computation of activation level is
based on the values of each input signal received from a neighboring node, and the weights on each input link.
C. Support Vector Machine
SVMs were developed by Cortes & Vapnik (1995) for binary classification. The goal of SVM modeling is to find the
optimal hyperplane that separates clusters of vector in such a way that cases with one category of the target variable are on one
side of the plane and cases with the other category are on the other size of the plane. A Support Vector Machine (SVM)
performs classification by constructing an N-dimensional hyperplane that optimally separates the data into two categories. The
vectors near the hyperplane are the support vectors. Data points are projected into a higher-dimensional space where the data
points effectively become linearly separable (this projection is realized via kernel techniques). Fig. 12 presents an overview of
the SVM process for classification in linear separable case.
Figure 12. Classification using SVM (linear separable case)
ACKNOWLEDGMENT
This research was supported by IIUSE of Southeast University, by a grant provided by 1000 Program for the Recruitment
of Global Experts, and by a special grant, Shuangchuang, provided by Jiangsu Province. These support are gratefully
acknowledged.
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