Fall risk assessment of cantilever bridge projects using Bayesian network

Chen, Tung-Tsan; Leu, Sou-Sen

doi:10.1016/j.ssci.2014.05.011

Cited by 42 publications

(21 citation statements)

References 19 publications

(43 reference statements)

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“…Existing studies have applied BNs to safety assessments in several high-risk locations and tasks, such as oil and gas pipelines [26], natural gas stations [27], maritime work [28], hazmat transportation [29], and construction. Chen and Leu [30] assessed the probabilities of fall risks and their sensitive factors in bridge projects based on BN analysis. Wu et al [31] proposed a dynamic BN-based approach to provide support for safety analysis in tunnel construction.…”

Section: Applications Of a Bayesian Network In Occupational Safetymentioning

confidence: 99%

Exploring the Impact of Unsafe Behaviors on Building Construction Accidents Using a Bayesian Network

Guo

et al. 2019

IJERPH

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Unsafe behavior is a critical factor leading to construction accidents. Despite numerous studies supporting this viewpoint, the process by which accidents are influenced by construction workers’ unsafe behaviors and the extent to which unsafe behaviors are involved in this process remain poorly discussed. Therefore, this paper selects cases from Chinese building construction accidents to explore the probabilistic transmission paths from unsafe behaviors to accidents using a Bayesian network. First, a list of unsafe behaviors is constructed based on safety standards and operating procedures. Second, several chains of unsafe behaviors are extracted from 287 accident cases within four types (fall, collapse, struck-by and lifting) to form a Bayesian network model. Finally, two accidents are specifically analyzed to verify the rationality of the proposed model through forward reasoning. Additionally, critical groups of unsafe behaviors leading to the four types of accidents are identified through backward reasoning. The results show the following: (i) The time sequence of unsafe behaviors in a chain does not affect the final posterior probability of an accident, but the accident attribute strength of an unsafe behavior, affects the growth rate of the posterior probability of an accident. (ii) The four critical groups of unsafe behaviors leading to fall, collapse, struck-by, and lifting are identified. This study is of theoretical and practical significance for on-site behavioral management and accident prevention.

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Section: Applications Of a Bayesian Network In Occupational Safetymentioning

confidence: 99%

Exploring the Impact of Unsafe Behaviors on Building Construction Accidents Using a Bayesian Network

Guo

et al. 2019

IJERPH

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“…The FMECA method is used to analyze fault modes of function modules or parts of the system obtained by employing SADT method, which provides bases for states of variables in BN model. The specific establishment method can refer to [19][20][21].…”

Section: Directed Edge a Of Frmmentioning

confidence: 99%

Fault Inference of Electronic Equipment Based on Multi-State Fuzzy Bayesian Network

et al. 2019

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The aim of this study is to extend the directive function of fault inference in test and diagnosis system for electronic equipment. There are many problems, such as presence of various types of uncertain information in test set of electronic equipment, frequent degenerative faults, complex relationships of modules, multiple fault modes, existence of fuzzy interval in fault state, and interaction of each module. In view of these problems, the total membership degree of faults is commonly synthesized based on weights of multiple test indicators and normal membership degree of a single indicator. On this basis, this study builds the model for inferring fault states of leaf and root nodes based on multi-state triangular fuzzy Bayesian network (BN). Finally, this research carried out feasibility analysis on fault inference of a super-heterodyne receiver, thus verifying the efficiency and applicability of the method proposed in the study.

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“…The determination of JPD and CPT is critical for the establishment of Bayesian network. There are generally three approaches to constructing a Bayesian network: (1) learning from quantities of training data; (2) depending on experiences of domain experts; and (3) hybrid method (Chen and Leu, ). The second approach is chosen in this study as the training data are limited, and Delphi technology (Billig and Thrän, ) is adopted to build a consensus on the determination of JPD and CPT.…”

Section: Case Studymentioning

confidence: 99%

“…(Hong et al., ; Aliahmadi et al., ; Nývlt et al., ; Jurado et al., ). As a common approach to quantitatively analyze risk occurrence probability, Bayesian network has been widely used in construction projects to study slope collapse (Cheng and Hoang, ), fall risk of cantilever bridge projects (Chen and Leu, ), deep water drilling operations (Bhandari et al., ), etc., and has also been applied to other diverse research fields (Sun and Bette, ; Wang et al., ; Yuen and Mu, ; Mu and Yuen, ). It not only combines the merits of fault tree and other risk probability quantitative analysis methods, but also has distinct advantages in describing dependence between variables quantitatively (Holický et al., ), and dealing with uncertainty information (Lee et al., ).…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Risk Analysis of Diversion Tunnel Construction Simulation

Zhong

Ren

et al. 2017

Computer aided Civil Eng

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Comprehensive and effective risk analysis is significant for studying construction simulation of diversion tunnel. Existing tunnel risk simulation approaches mainly analyze ordinary risk factors, and cannot quantitatively study risk events considering their causes. Additionally, in other tunnel probabilistic risk analysis methods, although some studies have made full probabilistic estimates of tunnel schedule, risk factors are unable to be studied considering cyclic construction characteristics and occurrence probability of risk events cannot be determined quantitatively. To address the issues, a probabilistic risk analysis approach of diversion tunnel construction simulation is proposed. Based on hierarchical simulation model, risk factors can be analyzed at operation level of tunnel construction. Moreover, Bayesian network is embedded into simulation program to quantitatively calculate probability of risk events in each simulation cycle, considering geology, design, construction, and management conditions and their mutual dependence.C 2017 Computer-Aided Civil and Infrastructure Engineering.

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Fall risk assessment of cantilever bridge projects using Bayesian network

Cited by 42 publications

References 19 publications

Exploring the Impact of Unsafe Behaviors on Building Construction Accidents Using a Bayesian Network

Exploring the Impact of Unsafe Behaviors on Building Construction Accidents Using a Bayesian Network

Fault Inference of Electronic Equipment Based on Multi-State Fuzzy Bayesian Network

Probabilistic Risk Analysis of Diversion Tunnel Construction Simulation

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