Data mining of the essential causes of different types of fatal construction accidents. Issue 2 (February 2023)

• Record Type: Journal Article
• Authors: Rafindadi, Aminu Darda'u; Shafiq, Nasir; Othman, Idris; Ibrahim, Ahmad; Aliyu, M.M.; Mikić, Miljan; Alarifi, Hamzh
• Is Part Of: Heliyon. Volume 9:Issue 2(2023)
• Publication Date: 2023-02
• Subjects: Data mining -- Association rule -- Fatal construction accidents -- Types of construction accidents -- Causes -- Construction industry; Research -- Periodicals; Medical sciences -- Periodicals; Natural history -- Periodicals; Social sciences -- Periodicals; Earth sciences -- Periodicals; Physical sciences -- Periodicals; 507.2
• DOI: 10.1016/j.heliyon.2023.e13389
• Languages: English
• ISSNs: 2405-8440

Abstract

Abstract: Accident analysis is used to discover the causes of workplace injuries and devise methods for preventing them in the future. There has been little discussion in the previous studies of the specific elements contributing to deadly construction accidents. In contrast to previous studies, this study focuses on the causes of fatal construction accidents based on management factors, unsafe site conditions, and workers' unsafe actions. The association rule mining technique identifies the hidden patterns or knowledge between the root causes of fatal construction accidents, and one hundred meaningful association rules were extracted from the two hundred and fifty-three rules generated. It was discovered that many fatal construction accidents were caused by management factors, unsafe site circumstances, and risky worker behaviors. These analyses can be used to demonstrate plausible cause-and-effect correlations, assisting in building a safer working environment in the construction sector. The study findings can be used more efficiently to design effective inspection procedures and occupational safety initiatives. Finally, the proposed method should be tested in a broader range of construction situations and scenarios to ensure that it is as accurate as possible.