Developing an Ensemble Predictive Safety Risk Assessment Model: Case of Malaysian Construction Projects

Haleh Sadeghi, Saeed Reza Mohandes, M. Reza Hosseini, Saeed Banihashemi, Amir Mahdiyar and Arham Abdullah
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Haleh Sadeghi: Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China
Saeed Reza Mohandes: Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China
M. Reza Hosseini: School of Architecture and Built Environment, Deakin University, Geelong 3217, VIC, Australia
Saeed Banihashemi: Department of Building and Construction Management, University of Canberra, Bruce 2617, ACT, Australia
Amir Mahdiyar: School of Housing, Building and Planning, Universiti Sains Malaysia, Penang 11800, Malaysia
Arham Abdullah: Universiti Malaysia Kelantan, Beg Bercunci No. 01, Bachok, Kelantan 16300, Malaysia

IJERPH, 2020, vol. 17, issue 22, 1-25

Abstract: 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.

Keywords: construction hazard; safety risk management; site management; data mining; neural network; fuzzy inference system; ANFIS; Malaysia (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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