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. Attached Files Name Description MIMEType Size Downloads Title Developing an Ensemble Predictive Safety Risk Assessment Model: Case of Malaysian construction projects Author(s) Sadeghi, Haleh Mohandes, Saeed Reza 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 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment School of Architecture and Built Environment Open Access Collection Related Links Link Description Link to full-text (open access)     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. 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. Versions Version Filter Type Mon, 23 Nov 2020, 07:16:27 EST Mon, 23 Nov 2020, 10:51:19 EST Tue, 24 Nov 2020, 04:00:54 EST Tue, 24 Nov 2020, 05:08:50 EST Tue, 24 Nov 2020, 10:24:56 EST Tue, 24 Nov 2020, 10:27:17 EST Tue, 24 Nov 2020, 15:00:34 EST Sat, 28 Nov 2020, 01:44:14 EST Sat, 09 Jan 2021, 01:33:34 EST Filtered Full Citation counts:   Cited 2 times in TR Web of Science Cited 2 times in Scopus Search Google Scholar Access Statistics: 70 Abstract Views, 1 File Downloads  -  Detailed Statistics Created: Mon, 23 Nov 2020, 10:51:19 EST

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.