A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Comparative Analysis of the Patterns of Unsafe Behaviors in Accidents between Building Construction and Urban Railway Construction
Building and urban railway construction are two typical high-risk types of construction in which unsafe behaviors have a considerable impact on the occurrence of accidents. To explore the patterns of unsafe behaviors and promote accident prevention, a comparative analysis is conducted to analyze the behavioral risk chains of accidents for these two construction types in the Chinese construction industry using complex network (CN) theory. First, accident cases are collected from government websites, and unsafe behaviors are extracted and classified according to a summary list of several safety standards and operating procedures. Second, behavioral risk chain networks of accidents (BRCNAs) for the two construction types are established as directed networks. Pajek, a program for visualizing and analyzing large networks, is then used to construct network models. Finally, the topological parameters of the BRCNAs are calculated and compared. The results show that BRCNAs are both scale-free and small-world networks. The characteristics of the building construction BRCNA are related to individual workers, and the characteristics of the urban railway construction BRCNA are related to the working environment and supervisors. However, there are certain unsafe behaviors that play an important role in accidents in both construction types. These findings reveal the variations in interactions among unsafe behaviors that cause accidents in the two construction types to further understand the patterns of unsafe behaviors. This study is of theoretical and practical significance for improving accident causation models and supporting on-site safety management in the construction industry.
Comparative Analysis of the Patterns of Unsafe Behaviors in Accidents between Building Construction and Urban Railway Construction
Building and urban railway construction are two typical high-risk types of construction in which unsafe behaviors have a considerable impact on the occurrence of accidents. To explore the patterns of unsafe behaviors and promote accident prevention, a comparative analysis is conducted to analyze the behavioral risk chains of accidents for these two construction types in the Chinese construction industry using complex network (CN) theory. First, accident cases are collected from government websites, and unsafe behaviors are extracted and classified according to a summary list of several safety standards and operating procedures. Second, behavioral risk chain networks of accidents (BRCNAs) for the two construction types are established as directed networks. Pajek, a program for visualizing and analyzing large networks, is then used to construct network models. Finally, the topological parameters of the BRCNAs are calculated and compared. The results show that BRCNAs are both scale-free and small-world networks. The characteristics of the building construction BRCNA are related to individual workers, and the characteristics of the urban railway construction BRCNA are related to the working environment and supervisors. However, there are certain unsafe behaviors that play an important role in accidents in both construction types. These findings reveal the variations in interactions among unsafe behaviors that cause accidents in the two construction types to further understand the patterns of unsafe behaviors. This study is of theoretical and practical significance for improving accident causation models and supporting on-site safety management in the construction industry.
Comparative Analysis of the Patterns of Unsafe Behaviors in Accidents between Building Construction and Urban Railway Construction
Guo, Shengyu (author) / Tang, Bing (author) / Liang, Kongzheng (author) / Zhou, Xinyu (author) / Li, Jichao (author)
2021-02-19
Article (Journal)
Electronic Resource
Unknown
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