Design, Construction, and Maintenance of Bridges GSP 251 © ASCE 2014
Falling Risk Assessment of Advanced Shoring Method Bridge
Construction Projects
Tung-Tsan Chen1, C.C. Wang 2
1
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2
Associate Professor, Department of Civil Engineering and Engineering Management, National
Quemoy University, No.1 University Rd., Kinmen County, 892, Taiwan, R.O.C., Email:
tungtsan@nqu.edu.tw
Associate Professor, Department of Civil Engineering and Engineering Informatics, Cheng Shiu
University, Kaohsiung, 833, Taiwan, R.O.C. Email: ccw@csu.edu.tw
ABSTRACT: Falling accounts for the majority of accidents in a bridge construction
project. Site safety management today relies on checklist assessment, which is
dependent on the ability and experience of the evaluators. In addition, the method used
to carry out site safety management today could not address the most critical factors
which lead to site accidents.
In order to go beyond the limitations of the traditional checklist method, in this study
the factors of falling in construction site are analyzed in a Fault Tree (FT), and then the
FT is transformed into a Bayesian Network (BN). The BN enables us to explore the
probabilities of accidents happening. When compared with current cases of bridge
construction in Taiwan, it is worth noting that the result from the BN model agrees
with the result from traditional checklist method. In another word, BN model verified
to be a realistic and useful model to deal with site safety management problems.
INTRODUCTION
The construction of infrastructure in a nation directly affects every aspect of
citizens’ lives. This is especially true for communication and transportation. Improving
the working condition of construction site guarantees a smooth progression, avoiding
the various societal cost accidents might induce.
In recent years the design and construction of bridges have become more and
more complex. When there are faults in the design, the quality, cost, and even the
well-being of the on-site construction workers are affected negatively.
In response to the above problem, it is important to find the faults in bridge
construction, explore their relationships, and analyze the probability of occupational
hazard. This enables concerned construction agencies to come up with ways to avoid
accidents, thus reduce the chance of accidents in construction site.
Since falling accounts for the most accidents in construction site, this study
focuses on the application of Fault Tree Analysis and its transformation into a
Bayesian Network on the factors contributing to falling.
Design, Construction, and Maintenance of Bridges
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LETERATURE REVIEW
In this study Fault Tree Analysis is used to determine the factors that lead to
accidents and their relationships with each other, and the Bayesian Network is used to
overcome the problem of insufficient data. Therefore, a review of studies concerning
Fault Tree, Bayesian Network, and transformation of Fault Tree to Bayesian Network
is discussed here.
Fault Tree Analysis (FTA), which can effectively analyze the causal relationship
of fault event and probability technology. However, if there is ambiguity in many
factors (e.g., human error), it is difficult to accurately estimate the probability of
occurrence of the fault event. Rodak and Sillima (2012) combined Fault Tree with
probabilistic risk analysis, and applied the product to the management of underground
water.
Durga Rao (2009) applied Dynamic Fault Tree to simulate the complex
interactions in a construction system, in order to test for its reliability.
Fu-Ming Chen issued in 2005 proposed a model that combines Fault Tree
Analysis, Fuzzy Set, and Analytic Hierarchy Process and assessed the risk in Braced
Excavation.
Yunng- Ru, Lai issued 2009 assessed the factors concerning pier degradation with
the Fuzzy Fault Tree.
Many researchers apply the Bayesian Network on education and examination.
Mislevy, Steinberg, and Almond (2002) and Mislevy, Almond & Lukas (2003)
proposed Evidence-Centered Design (ECD), which relies on Bayesian Network for its
assessment and analysis.
Combined Bayesian Network with experts’ opinions, acquiring the probability
values in the Bayesian Network subjectively, according to the conscious of the experts,
Bayesian Networks allow follow-up by high reliability numerical probability can be
analyzed (Xiao etc., 2008).
STATISTIC ON OCCUPATIPNL ACCIDENT IN TAIWAN
In Taiwan, the field of construction has higher occupational hazard than other
fields of work (Figure 1.) According to the statistics of Council of Labor Affairs, in 88
construction related cases from the past 10 years, there are 97 death, 66 severely
injured, and 5 lightly injured. Among these cases falling is the most often the cause for
accidents. The causes for falling could be various, such as unsafe work behavior, tight
work schedule, or faulty equipment etc.
Design, Construction, and Maintenance of Bridges
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Design, Construction, and Maintenance of Bridges GSP 251 © ASCE 2014
16
0.24
0.22
All industry
0.223
Manufacturing
0.21
0.2
Construction
0.188
Fatalities per 1000
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0.18
0.172
0.175
0.161
0.16
0.14
0.129
0.131
0.128
0.12
0.123
0.109
0.1
0.08
0.06
0.077
0.063
0.069
0.067
0.065
0.059
0.04
0.05
0.041
0.02
2000
2001
2002
2003
0.044 0.045
0.038 0.033 0.035 0.034
0.03 0.038 0.035 0.034 0.036
2004
2005
2006
2007
2008
0.036
0.028
2009
0.035
2010
Year
Fig. 1. Fatalities per 1000 persons in construction industry and all industries
(excluding deaths from occupational disease and traffic accidents), 2000~2010.
FALL RISK ASSESSMENT BASED ON BAYESIAN NETWORK
For establishing a better model of the Bayesian Network for Fall Risk
Assessment, 36 experts were surveyed with a questionnaire that has 97 items. They
were to grade these items according to their experience and knowledge. The result is
then tested on four bridge construction project (Advance Shoring Method).
Building a Fault Tree frame
In the FTA, falling is placed as the top event. According to the safety
management based on Domino Theory (Heinrich etc., 1980), the reasons for falling
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Design, Construction, and Maintenance of Bridges GSP 251 © ASCE 2014
could be categorized as place of incident and its condition, indirect causes, and basic
causes. These factors and the top event (falling) are related through the use of logic
gates.
Through experts’ opinions and review of related studies, two major causes that
lead to falling are:
(1) Improper set-up of the steel frame support;
(2) Improper dissemble of the steel frame support. If needed, these factors could
be further divided into sub-factors.
Lastly, according to the Domino Theory and Safety management (Jitwasinkul,
Hadikusumo 2011; Lingard, Rowlinson 2005), the four basic causes for occupational
accidents are: insufficient safety training, improper site management, improper health
and safety management, and improper health and safety planning. A Fault Tree is
established when the interaction of these basic causes, along with indirect causes, are
defined with the help of occupational accident logs and expert opinions (As shown in
Figure 1 and Figure 2).
From Fault Tree Analysis transfer Bayesian Network
The construction of the FT follows a top/down design. It’s first to identify a
particular undesired event as a top event, and then proceed from the event to its causes
until the primary events are reached. The relationships between events and causes are
commonly defined and represented by means of 「AND 」or 「OR」 logic gates
The transformation from FT into BN includes graphical and numerical tasks. In
the graphical mapping, primary events, intermediate events, and the top event of FT
are represented as root nodes, intermediate nodes, and the leaf node in the
corresponding BN, respectively. The nodes of a BN are connected in the same way as
corresponding components in FT. In the numerical mapping, the occurrence
probabilities of the primary events are assigned to the corresponding root nodes as
prior probabilities. For each intermediate node and leaf node, a conditional probability
table (CPT) is developed. The CPTs are developed according to the type of gate.
Finally, the Fault Tree is transformed into a Bayesian Network. In this process
overlapping nodes are integrated into the same node. In addition, meaningful
supplemental arrows are added between nodes based on experts’ opinion.
Design, Construction, and Maintenance of Bridges
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Design, Construction, and Maintenance of Bridges GSP 251 © ASCE 2014
Fig.1. Overall FT of falling accidents of advanced shoring method bridge (1)
Fig.2. Overall FT of falling accidents of advanced shoring method bridge (Cont.)
Design, Construction, and Maintenance of Bridges
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Design, Construction, and Maintenance of Bridges GSP 251 © ASCE 2014
Code
T
G1
G2
G3
F1
Risk factors
FTA of Falling Risk of Occupational hazards
Incorrect operating of supporting steel
Poor deck structure construction
Improper lifting operations
Poor assembly of supporting steel
Code
C9
C10
C11
C12
C13
F2
Improper removal supporting steel
C14
F3
F4
F5
E1
E2
Poor formwork operating
Improper operation of concrete pouring
Improper pre-stressed operating
Steel cables operator error
Lifting material collision
C15
B1
B2
B3
B6
D1
improper pier column top operation
B7
D2
D3
D4
D5
D6
C1
C2
C3
C4
Poor installation of supporting frame
Improper main truss installation
Improper main truss removal
Crane operator error
No guide cable
No safety belt
Up and down channel blockage
Irregularities of Working car
Poor safety belt belts and rings
Improper environment operation (e.g. Strong
wind and rain)
Safety equipment improperly
Climbing in hanging objects
No safety net
B8
B10
B11
B12
B13
B14
B16
B17
A1
Risk factors
Not guide rope
Not anti-drop railing
Work belt not fastened properly
Workers behind lifting jack
No intermediate support
Incorrectly command of hanging
personnel
Personnel to operate error
Improper control procedure
Incorrect procedure
Dangerous procedures or method
Improper activities or posture
Non-compliance with the Code of
Practice
Operation error
Work site disorder
Scattered material
No safety equipment
Unsafe equipment
No personal protective equipment
Not implement of self-management
Lack of safety and health signs
H / S Training and education
A2
H / S Environmental maintenance
A3
A4
Planning of the H / S
Management of the H / S
C5
C6
C7
C8
MODEL VERIFICATION
The results from applying this study’s proposed method on four bridge
construction projects can be seen in Table 1.
In the process of this study, every construction project’s safety assessment is
carried out by using safety assessment checklist. The probabilities for the four basic
causes are determined subjectively. After the post probabilities of intermediate nodes
are calculated, the result is plugged into ArgenaRisk. It is noted that the resultant
probabilities from the Bayesian Network tightly resembles those from the realistic
safety checklist.
The four construction projects are assessed monthly for their safety evaluation
record. The number one project in safety is indeed without flaw in its safety
management. In contrast, the project that ranked last in safety management is
Design, Construction, and Maintenance of Bridges
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Design, Construction, and Maintenance of Bridges GSP 251 © ASCE 2014
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projected to have a falling risk of 81.264%. Through the application and comparison
of BN model on these four construction projects, it is confirmed that a BN model is
applicable and reliable in carrying out safety management in bridge construction
(Advance Shoring Method).
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Table 1. Comparison between BN and real site assessment
Project No.
Fall risk (%)
from BN
Risk rank
By BN
Real site
assessment (score)
Real safety
rank
1
30.069
4
89.65
4
2
46.358
2
82.34
2
3
81.264
1
76.06
1
4
45.471
3
86.62
3
CONCLUSIONS
In this study a process is introduced, first as a Fault Tree Analysis that addresses
the fall risk in a bridge construction project (Advance Shoring Method). Later, it is
then transformed into a Bayesian Network to further explore the factors concerning
falling.
In the process of transforming the Fault Tree into a Bayesian Network,
meaningful arrows between nodes are added based on experts’ opinions. Lastly, the
logic gates in the FTA are transformed into the probabilities in a BN model, which is
then tested on the four bridge construction projects.
After comparing the result from the model with the result from the traditional
checklist method, the BN model proves to be a reliable and accurate method in dealing
with fall risk management. Therefore, a BN model as proposed in this study is able to
reduce the risk of falling, given such conditions.
REFERENCES
Chen Fu-Min, "The risk analysis of the building retained excavation accident",
master's thesis, National Kaohsiung University of Applied Sciences, Kaohsiung
(2005).
Design, Construction, and Maintenance of Bridges
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Design, Construction, and Maintenance of Bridges GSP 251 © ASCE 2014
Durga Rao, K., Gopika, V., Sanyasi Rao, V.V.S., Kushwaha, H.S., Verma, A.K.,
Srividya, A.,“Dynamic fault tree analysis using Monte Carlo simulation in
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Heinrich, H.W., Petersen, D. & Roos, N. (1980), Industrial Accident Prevention, 5th
ed., New York: McGraw-Hill.
Jitwasinkul, B.; Hadikusumo, B.H.W. Identification of Important organizational
factors influencing safety work behaviours in construction projects, Journal of Civil
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Lai Yin-lu, "Fuzzy Fault Tree Analysis of bridge piers deterioration ", master's Thesis,
National Cheng Kung University, Tainan, Taiwan (2009).
Lingard, H.; Rowlinson, S.,“Occupational Health and Safety in Construction Project
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Mislevy, R.J., Almond, R.G., Lukas, J. F, A brief introduction to evidence-centered
design, pp.8-19(2003).
Mislevy, R.J., Almond, R.G.,& Steinberg, L.S., Enhancing the design and delivery of
assessment systems: A four-process architecture, Journal of Technology, Learning,
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Rodak, C., Silliman, S.,“Probabilistic risk analysis and fault trees: Initial discussion of
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Xiao, L., Haijun, L., Lin, L., “Building method of diagnostic model of bayesian
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