Department of Occupational Safety and Health Engineering, Faculty of Health, Research Center for Environmental Pollutants, Qom University of Medical Sciences, Qom, Iran.
Department of Ergonomics, Health in Emergency and Disaster Research Center, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran.
Work. 2023;75(4):1341-1350. doi: 10.3233/WOR-220128.
The data mining of construction accidents based on a robust modeling process can be used as a practical technique for reducing the frequency of construction accidents.
This study was designed to data-mine construction accidents.
This study was conducted in 2020 on construction accidents in Iran for ten years (2009-2018). The instruments to collect the required data were the checklists and descriptive reports of the accidents. The dependent variables of the study included reactive safety indicators related to construction accidents (lost working days (LWD) and total accident costs (TAC)). The independent variables consisted of four latent factors: personal variables, organizational variables, unsafe working conditions, and unsafe acts. The data were collected based on the conceptual model designed for data mining. The data mining process was carried out based on the structural equation modeling by IBM AMOS V. 23.0.
A total of 5742 construction accidents occurring in 10 years were analyzed. The means of TAC and LWD indicators were estimated to be 248.20±52.60 days and 1893.10±152.22 $. These two indicators directly correlated with the two latent factors of unsafe conditions and unsafe acts and their related variables and were indirectly influenced by latent personal and organizational factors. The relationship between unsafe conditions and unsafe acts was significantly positive. The relationship between latent personal and organizational factors and the two construction accident indicators was significantly negative (p <0.05).
The model results showed that personal and organizational variables could, directly and indirectly, affect reactive safety indicators in construction projects. Thus, these findings can be used to design and improve safety strategies to prevent and decrease construction accidents and incidents.
基于稳健建模过程对建筑事故进行数据挖掘，可作为降低建筑事故频率的实用技术。
本研究旨在对建筑事故进行数据挖掘。
本研究于 2020 年对伊朗十年（2009-2018 年）的建筑事故进行研究。收集所需数据的工具是事故检查表和描述性报告。本研究的因变量包括与建筑事故相关的反应性安全指标（丧失工作日数（LWD）和总事故成本（TAC））。自变量包括四个潜在因素：个人变量、组织变量、不安全工作条件和不安全行为。数据是根据数据挖掘设计的概念模型收集的。数据挖掘过程是基于 IBM AMOS V.23.0 的结构方程模型进行的。
分析了 10 年内发生的 5742 起建筑事故。TAC 和 LWD 指标的平均值估计为 248.20±52.60 天和 1893.10±152.22 美元。这两个指标与不安全条件和不安全行为及其相关变量的两个潜在因素直接相关，并间接受到潜在个人和组织因素的影响。不安全条件与不安全行为之间的关系呈显著正相关。潜在个人和组织因素与两个建筑事故指标之间的关系呈显著负相关（p<0.05）。
模型结果表明，个人和组织变量可以直接和间接地影响建筑项目的反应性安全指标。因此，这些发现可用于设计和改进安全策略，以预防和减少建筑事故和事件。
