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Construction accident data mining: A retrospective study using structural equation modeling based on 10-year data
Ahmad Soltanzadeh1, Iraj Mohammadfam2, Mohsen Mahdinia1
1Department of Occupational Safety and Health Engineering, Faculty of Health, Research Center for Environmental Pollutants, Qom University of Medical Sciences, Qom, Iran.
Work (Reading, Mass.)
|January 30, 2023
Summary
Data mining construction accidents reveals personal and organizational factors significantly impact safety. Improving these areas can reduce accident rates and costs, enhancing overall construction safety.
Area of Science:
- Occupational Safety and Health
- Data Mining
- Construction Management
Background:
- Data mining construction accidents offers a practical approach to reducing incident frequency.
- Robust modeling is essential for effective accident data analysis.
Purpose of the Study:
- To perform data mining on construction accidents in Iran.
- To identify key factors influencing accident indicators.
Main Methods:
- Analysis of 5742 construction accidents in Iran (2009-2018).
- Utilized structural equation modeling (SEM) with latent factors: personal, organizational, unsafe conditions, and unsafe acts.
- Dependent variables included lost working days (LWD) and total accident costs (TAC).
Main Results:
- Unsafe conditions and unsafe acts directly correlated with higher LWD and TAC.
- Personal and organizational factors indirectly influenced LWD and TAC.
- A significant positive relationship was found between unsafe conditions and unsafe acts (p <0.05).
Conclusions:
- Personal and organizational variables significantly affect reactive safety indicators in construction.
- Findings support the development of targeted safety strategies to mitigate construction accidents.
- The study highlights the importance of addressing human and systemic factors in construction safety.
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