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Developing an ensemble predictive safety risk assessment model: Case of Malaysian construction projects

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Version 1 2020-11-23, 08:16
journal contribution
posted on 2024-06-19, 00:17 authored by H Sadeghi, SR Mohandes, MR Hosseini, S Banihashemi, A Mahdiyar, A Abdullah
Occupational Health and Safety (OHS)-related injuries are vexing problems for construction projects in developing countries, mostly due to poor managerial-, governmental-, and technical safety-related issues. Though some studies have been conducted on OHS-associated issues in developing countries, research on this topic remains scarce. A review of the literature shows that presenting a predictive assessment framework through machine learning techniques can add much to the field. As for Malaysia, despite the ongoing growth of the construction sector, there has not been any study focused on OHS assessment of workers involved in construction activities. To fill these gaps, an Ensemble Predictive Safety Risk Assessment Model (EPSRAM) is developed in this paper as an effective tool to assess the OHS risks related to workers on construction sites. The developed EPSRAM is based on the integration of neural networks with fuzzy inference systems. To show the effectiveness of the EPSRAM developed, it is applied to several Malaysian construction case projects. This paper contributes to the field in several ways, through: (1) identifying major potential safety risks, (2) determining crucial factors that affect the safety assessment for construction workers, (3) predicting the magnitude of identified safety risks accurately, and (4) predicting the evaluation strategies applicable to the identified risks. It is demonstrated how EPSRAM can provide safety professionals and inspectors concerned with well-being of workers with valuable information, leading to improving the working environment of construction crew members.

History

Journal

International Journal of Environmental Research and Public Health

Volume

17

Article number

ARTN 8395

Pagination

1-25

Location

Switzerland

Open access

  • Yes

ISSN

1661-7827

eISSN

1660-4601

Language

English

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2020, The Authors

Issue

22

Publisher

MDPI