A Study on the Cascade Evolution Mechanism of Construction Workers’ Unsafe Behavior Risk Factors
Author(s): |
Xin Luo
Yanjuan Tang Jun Zhou Mingru Wang Yong Tian |
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Medium: | journal article |
Language(s): | English |
Published in: | Buildings, 23 July 2024, n. 8, v. 14 |
Page(s): | 2483 |
DOI: | 10.3390/buildings14082483 |
Abstract: |
There are numerous risk factors across various dimensions that lead to unsafe behaviors among construction workers, and the interactions between these factors are complex and intertwined. Therefore, it is crucial to comprehensively explore the mechanisms of these risk factors across all dimensions to reduce the accident rate. This paper combines cascading failure and entropy flow models to construct a cascading trigger model for identifying key nodes and paths in a risk network. First, this paper identifies the risk factors in the individual, organizational, managerial, and environmental dimensions, dividing them into deep and surface factors. Based on this, a risk network is constructed, and cascading failure is introduced to simulate the dynamic evolution of risks. Then, the entropy flow model is introduced to quantify the risk flow in risk propagation. Finally, to address the uncertainty of risk occurrence, Visual Studio Code is used for coding, and a simulation platform is built using JavaScript. After conducting simulation experiments, the results are statistically analyzed. The results show that the key nodes of deep factors are mainly concentrated in the individual dimension (herd mentality, negative emotions, physical fatigue, fluke mindset), organizational dimension (poor cohesion, poor internal communication), and managerial dimension (abusive leadership style and insufficient/low-quality safety education and training); the surface factors are mainly the poor safety climate in the organizational dimension. The findings provide theoretical support for reducing the accident rate caused by unsafe worker behaviors, aiming to reduce accident risk losses by cutting off risk propagation paths. |
Copyright: | © 2024 by the authors; licensee MDPI, Basel, Switzerland. |
License: | This creative work has been published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met. |
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data sheet - Reference-ID
10795303 - Published on:
01/09/2024 - Last updated on:
01/09/2024