Exploring the behavioral risk chains of accidents using complex network theory in the construction industry

Guo, Shengyu; Zhou, Xinyu; Tang, Bing; Gong, Pulin

doi:10.1016/j.physa.2020.125012

Cited by 59 publications

(31 citation statements)

References 28 publications

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“…The most distant node pairs of the network are v32 and v150 or v7 and v45. Compared with some accident networks studied in the past [15,34,35] , the diameter of I-FRPN is larger, and the evolution path of the risk is complicated.…”

Section: Network Diameter and Average Path Lengthmentioning

confidence: 99%

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Propagation Network of Tailings Dam Failure Risk: An Empirical Research and The Identification of Key Hazard Node

Zhen¹,

Ma²,

Zhao³

2021

Preprint

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The tailings dam system is complex, and the dam structure changes continuously over time, which makes it difficult to identify hazards and analyze the causes of failure accidents. This paper uses hazards to represent the nodes, and the relationship between hazards to represent the edges. Based on the complex network theory, the propagation network of tailings dam failure risk is constructed. The traditional identification methods usually focus on one aspect of the information of the network, while it cannot take into account to absorb the advantages of different methods, resulting in the lack of information, which will lead to a certain difference between identified key hazards and real key hazards. In order to solve this problem, by absorbing the advantages of different methods under different hazard remediation (deleted) ratios, combined with the characteristics of multi-stage propagation of tailings dam failure risk, this paper proposes a multi-stage collaborative hazard remediation method (MCHRM) to determine the importance of hazard nodes. When the important nodes of this network that affect the network efficiency are found, by consulting the monitoring data, daily inspection results and safety evaluation information of each hazard before the dam failure, we can determine the real cause of the accident from the above important nodes according to the grading standards of hazard indicators. In the application example of Feijão Dam I, this article compares the key hazards obtained by the above methods with the conclusions of the accident investigation team. It can be found that the above method has a very good effect on finding the key causes of tailings dam failure.

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Section: Network Diameter and Average Path Lengthmentioning

confidence: 99%

“…The clustering coe cient of the I-FRPN refers to the degree of interconnection between adjacent nodes of a hazard node in the network [34] . That is to say, there is no clustering coe cient for nodes with a degree value of 1.…”

Section: Clustering Coe Cient and Small-world Propertymentioning

confidence: 99%

Propagation Network of Tailings Dam Failure Risk: An Empirical Research and The Identification of Key Hazard Node

Zhen¹,

Ma²,

Zhao³

2021

Preprint

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show abstract

“…Moreover, the performance of unsafe behaviors was usually associated with risk assessment, in which the features of behaviors were embodied through the expert system and machine learning, such as distance, area, and speed in collision events [ 19 , 20 ]. To quantify the unsafe behaviors, researchers, in part, showed that unsafe behaviors were the outcome of exceeding the acceptable threshold, emphasizing the correlation between behaviors and system standards [ 21 , 22 ]. However, the determination of the standards should be evaluated in combination with the severity of the outcomes of the maritime events, which was empirical subjectivity [ 23 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Identifying Cognitive Mechanism Underlying Situation Awareness of Pilots’ Unsafe Behaviors Using Quantitative Modeling

Jiang

Chen

Kang

et al. 2021

IJERPH

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Situation awareness (SA) of pilots’ unsafe behavior can ensure safety onboard. Thus, the cognitive mechanism that controls the SA leading to unsafe behavior must be articulated. This study employs the SA model and theory of planned behavior (TPB) to articulate a quantitative model of ship safe piloting. Firstly, the hierarchical classification framework of unsafe behaviors was constructed as an analytical foundation for rational and unconscious behaviors in sight of cognitive processes, and then the measurement elements of the cognitive mechanisms for behaviors were identified. Subsequently, based on the structural model, a hypothetical model of the cognitive path for unsafe behaviors was proposed by using the extended TPB, where there are four independent variables (i.e., attitude (ATD), subjective norm (SN), and perceived behavioral control (PBC)), one mediating variables (i.e., SA) and two dependent variables (i.e., behavioral intention (BI) and unsafe behaviors (BE)). Finally, this hypothetical model was analyzed with the data resources from extended TPB questionnaire of 295 pilots. Analysis results show that relationships of causation and mediation in the cognitive mechanism are in line with the behavior pattern and SA have a pronounced mediating effect and a strong relevance to the causal chain of extended TPB framework. This study integrated the SA three-level model to understand the motivation–cognition–action–feedback (MCAF) mechanism of pilots’ unsafe behaviors under cognitive mode of information processing through structural model. It would make a valuable contribution to the assessment and intervention of safety behaviors, and provide a basic framework for monitoring the situation awareness of pilot by man-machine interactive measurement technology in the future.

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“…Various equipment, machines, and human resources are interrelated in railway systems, which increase the complexity of railway accidents [ 22 , 23 ]. Therefore, network-based accident causation models have been developed to analyze the causes of accidents and model interdependent hazards in recent years [ 24 ]. The nodes in the network represent hazard events or accidents, and the links indicate the relationship between hazards and accidents.…”

Section: Introductionmentioning

confidence: 99%

Causality-Network-Based Critical Hazard Identification for Railway Accident Prevention: Complex Network-Based Model Development and Comparison

Zhang

Peng

2021

Entropy

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This study investigates a critical hazard identification method for railway accident prevention. A new accident causation network is proposed to model the interaction between hazards and accidents. To realize consistency between the most likely and shortest causation paths in terms of hazards to accidents, a method for measuring the length between adjacent nodes is proposed, and the most-likely causation path problem is first transformed to the shortest causation path problem. To identify critical hazard factors that should be alleviated for accident prevention, a novel critical hazard identification model is proposed based on a controllability analysis of hazards. Five critical hazard identification methods are proposed to select critical hazard nodes in an accident causality network. A comparison of results shows that the combination of an integer programming-based critical hazard identification method and the proposed weighted direction accident causality network considering length has the best performance in terms of accident prevention.

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Exploring the behavioral risk chains of accidents using complex network theory in the construction industry

Cited by 59 publications

References 28 publications

Propagation Network of Tailings Dam Failure Risk: An Empirical Research and The Identification of Key Hazard Node

Propagation Network of Tailings Dam Failure Risk: An Empirical Research and The Identification of Key Hazard Node

Identifying Cognitive Mechanism Underlying Situation Awareness of Pilots’ Unsafe Behaviors Using Quantitative Modeling

Causality-Network-Based Critical Hazard Identification for Railway Accident Prevention: Complex Network-Based Model Development and Comparison

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