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Development and validation of construction occupational safety and health risk assessment matrix for construction industry in Malaysia


Citation

Ranjan, Mokhtar Zamimi (2021) Development and validation of construction occupational safety and health risk assessment matrix for construction industry in Malaysia. Doctoral thesis, Universiti Putra Malaysia.

Abstract

The construction industry has a high-risk working environment in the context of occupational safety and health (OSH). The OSH risk assessment is the first and critical step towards supporting the decision-making process in the OSH plan to reduce occupational accident. Most of the conventional risk assessment methods, particularly common RAM, cannot produce an accurate result, which may increase the risk. This research aims to establish and validate the construction occupational safety and health risk assessment matrix (COSHRAM). The COSHRAM is developed based on the likelihood of an occupational construction accident (L), the consequences of an occupational construction accident (C) and the modifying risk factor of an occupational construction accident (MRF). The magnitude of the risk difference between common RAM and COSHRAM was compared in this study. This study also determined the relationship between the L, C and MRF with the residual risk (RR) value in the COSHRAM analysis. The study then investigates how the L, C and MRF predict the RR value. The study was divided into three phases. In phase one, a field survey was conducted to obtain the MRF of an occupational construction accident. Concurrently, construction accident data for ten years was acquired from the Department of Occupational Safety and Health (DOSH). During this phase, all of the data is analyzed using a descriptive method. In the second phase, the development and validation process of COSHRAM was carried out. The Fuzzy Delphi Method (FDM) was used to analyze COSHRAM validation data. Meanwhile, data were analyzed using paired sample t-tests, correlation analysis, and multiple linear regression in the third phase. Data in phases one and three were analyzed using version 25 of SPSS and version 16.3 of Microsoft Excel. The COSHRAM experts’ evaluation found that the average threshold value was 0.066, which was below 0.2. The overall percentage of each item evaluated by the panellists was 84%, which is greater than 75%, which indicates a good degree of validity. There were significant differences between the common RAM analysis and COSHRAM analysis results for each participating construction site. The COSHRAM analysis was also found to result in superior accuracy, almost twice the common RAM analysis. Additionally, the analytical results also found that risk value is overestimated when common RAM is used. At the same time, L (r-0.573, p<0.01), C (r-0.800, p<0.01) and MRF (r- 0.346, p<0.01) were also positively correlated with RR in the COSHRAM analysis. Furthermore, the regression analysis also indicated a significant RR values prediction by the L, C and MRF (p = 0.001, p < 0.05). The COSHRAM has successfully resolved the unavailable and unreliable data that had been misleading its users by providing clarity in determining the L rating scale. Furthermore, the incorporation of MRF in determining risk magnitude enables organizations to assess the efficacy of risk controls that have been applied. Therefore, the COSHRAM resulted in a better risk magnitude estimation and appears to be a reliable OSH risk assessment tool, particularly in the Malaysian construction industry.


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Additional Metadata

Item Type: Thesis (Doctoral)
Subject: Occupational Health
Subject: Risk Assessment
Call Number: FPSK(p) 2021 25
Chairman Supervisor: Associate Professor Mohd Rafee bin Baharudin, PhD
Divisions: Faculty of Medicine and Health Science
Keywords: Construction industry; Modifying risk factor; Occupational safety and health; Risk assessment matrix
Depositing User: Mas Norain Hashim
Date Deposited: 13 Jul 2022 08:04
Last Modified: 13 Jul 2022 08:04
URI: http://psasir.upm.edu.my/id/eprint/97959
Statistic Details: View Download Statistic

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