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Developing an Ensemble Predictive Safety Risk Assessment Model: Case of Malaysian construction projects

Sadeghi, Haleh, Mohandes, Saeed Reza, Hosseini, M. Reza, Banihashemi, Saeed, Mahdiyar, Amir and Abdullah, Arham 2020, Developing an Ensemble Predictive Safety Risk Assessment Model: Case of Malaysian construction projects, International Journal of Environmental Research and Public Health, vol. 17, no. 22, doi: 10.3390/ijerph17228395.

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Title Developing an Ensemble Predictive Safety Risk Assessment Model: Case of Malaysian construction projects
Author(s) Sadeghi, Haleh
Mohandes, Saeed Reza
Hosseini, M. RezaORCID iD for Hosseini, M. Reza orcid.org/0000-0001-8675-736X
Banihashemi, Saeed
Mahdiyar, Amir
Abdullah, Arham
Journal name International Journal of Environmental Research and Public Health
Volume number 17
Issue number 22
Article ID 8395
Total pages 25
Publisher MDPI AG
Place of publication Basel, Switzerland
Publication date 2020
ISSN 1661-7827
1660-4601
Keyword(s) ANFIS
Malaysia
construction hazard
data mining
fuzzy inference system
neural network
safety risk management
site management
Summary 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.
Language eng
DOI 10.3390/ijerph17228395
Indigenous content off
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2020, The Authors
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30145591

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.