Using the Pivot Pair-Wise Relative Criteria Importance Assessment (PIPRECIA) Method to Determine the Relative Weight of the Factors Affecting Construction Site Safety Performance

Abstract

In the construction industry, safety issues are considered major concerns. Despite recent attempts to improve safety in the construction sector, this sector is considered dangerous and unsafe. Construction safety management in Iraq is plagued by a high incidence of construction accidents, resulting in a higher number of injuries and fatalities. Creating a safety program is one strategy to alleviate this problem by making safety an intrinsic part of construction projects. Defining which factors are the most significant and have the greatest effect on safety performance is crucial to building a complete safety program. It ensures that construction companies are not wasting money on inadequate safety programs. As a result, this article aims to identify the critical safety factors that influence safety performance in Iraqi construction projects and assign a relative weight to them based on their importance. First, relevant literature was reviewed to identify the safety performance factors. Second, the Pivot Pairwise Relative Criteria Importance Assessment (PIPRECIA), a recently proposed technique for calculating criteria weights, was employed to determine the relative weight of factors. In this paper, a list of 21 sub-factors classified into 8 categories of main factors was identified. Finally, the findings of PIPRECIA show that the "Management Practices" factor has the top rank with a weight of 0.4221 among the other main safety factors. The results also showed that among the 21 sub-factors, the three with the highest weights, 0.0686, 0.0665, and 0.0619, belonged to "Personal protective equipment (PPE)", "First aid and medical care" and "Housekeeping program", which were under the "Management Practices" factor. Further, the "Regular safety inspections" and "Contractor safety rewards and punishment programs" sub-factors were identified as the least important sub-factors with weights of 0.0350 and 0.0333, respectively.

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“…Therefore, it is vital to identify the ideal variables for individual factors in the model prior to resource allocation, as this enables decision-makers to prioritize investments and allocate resources effectively. Ideal variable selection facilitates efficient resource allocation, particularly when resources are limited, and enables decision-makers to focus their efforts on areas that are likely to have the greatest impact on the desired outcome (Qaddoori & Breesam, 2023).…”