The construction industry has always been regarded as a notorious sector for its high number of casualties and fatalities. Despite the improvements made in the Occupational Health and Safety (OHS) within this industry, it still accounts for the highest number of occupational injuries compared to any other industries. Based on the mentioned fact, conducting more research on the area of OHS within the construction industry is vital in order to improve the involved workers’ well-being. Although relatively high numbers of studies have centralized on the OHS associated with the construction sector, some crucial gaps still exist that need to be bridged effectively as follows: (1) lack of a comprehensive risk assessment model embodying all the required steps, (2) paucity of a holistic...[ Read more ]

The construction industry has always been regarded as a notorious sector for its high number of casualties and fatalities. Despite the improvements made in the Occupational Health and Safety (OHS) within this industry, it still accounts for the highest number of occupational injuries compared to any other industries. Based on the mentioned fact, conducting more research on the area of OHS within the construction industry is vital in order to improve the involved workers’ well-being. Although relatively high numbers of studies have centralized on the OHS associated with the construction sector, some crucial gaps still exist that need to be bridged effectively as follows: (1) lack of a comprehensive risk assessment model embodying all the required steps, (2) paucity of a holistic safety risk assessment model that includes all the essential risk parameters towards assessing the OHS of construction workers, and (3) dearth of an OHS risk management framework related to the workers involved in the green building construction projects. All the mentioned shortcomings stated above lead to deteriorating the OHS of workers. Thus, this dissertation aims to solve the mentioned gaps through the exploitation of advanced fuzzy algorithms, through which the uncertainty associated with the statistical-based approaches could be solved.

Firstly, a Comprehensive Hybrid Fuzzy-based Occupational Risk Assessment Model (CHFORAM) is developed to systematically identify, analyze, and evaluate the safety risks posing danger to the involved workers using the integration of the Interval-Valued Fuzzy Delphi Method (IVFDM) with the Fuzzy Best Worst Method (FBWM) and the Risk Decision Matrix (RDM). The suitability of CHFORAM developed is verified through its application to the case building maintenance workers. It was observed that the developed CHFORAM could fully identify the critical safety risks posing danger to the maintenance workers, followed by precisely analyzing them using constrained optimization algorithm. Finally, the analyzed safety risks could be effectively evaluated using the proposed strategies.

Secondly, a Holistic Occupational Health and Safety Risk Assessment Model (HOHSRAM) is developed, in which all the essential risk parameters for assessing the safety level of workers are taken into consideration. The developed HOHSRAM is based on the integration of Logarithmic Fuzzy-based Analytical Network Process (LFANP) with the Interval-Valued Pythagorean Fuzzy Extended Technique for Order of Preference by Similarity to Ideal Solution (IVPFETOPSIS). To show the supremacy of the developed HOHSRAM against the other existing safety assessment approaches, the case of workers working with tower cranes is taken into account. It is observed that the developed HOHSRAM leads to the obtainment of much more diversified rankings as compared to other available assessment methods, through the consideration of decision makers’ importance weights as well as inclusion of all the essential risk parameters.

Furthermore, in order to improve the OHS within the green building construction projects, a Holistic Z-numbers-based Risk Management Framework (HZRMF) is developed. The developed framework is based on the amalgamation of Z-numbers-based Delphi technique (ZDM), Z-numbers-based Best Worst Method (ZBWM), and Z-numbers-based Technique for Order of Preference by Similarity to Ideal Solution (ZTOPSIS). Using Hong Kong green building construction projects as a case study, the developed HZRMF could precisely and systematically identify, analyze, evaluate, and control the risks to which the respective workers are exposed. From the results obtained, it is witnessed that fall-associated safety risks pose significant threats to the workers involved in the related green building construction activities.

All the models developed in this dissertation are proved to guide the concerned safety professionals and managers towards taking more prudent strategies for improving the working environment of construction sites, which in turn, this will contribute to significantly improving the well-being of the respective workers.