Root Factors and Effective Mitigation Methods For Construction Safety: An Analytical Empirical Model

Abstract

The construction industry plays a crucial role in contributing to the economy and developing sustainable infrastructure. In addition, it is known as one of the most dangerous industrial domains in the world. Over the years, special attention has been paid to developing models for managing and planning safety. Many research studies were conducted to develop models for preventing work accidents and mitigating their consequences. Usually after the occurrence of an accident, a learning process through investigation is required. To identify indirect systematical reasons for an accident, there is a need for an investigating model, which can identify, control, and resolve risk factors in the workplace. In most cases, the goal is to find the point on the timeline that starting from it, it is impossible to come back, whereas in construction sites this is much harder, mainly because of the influence of various risk factors. Assessing safety risks across various dimensions, from macro to micro levels, including project type (residential, roadways, bridges, etc.), construction stages (planning, site organization, excavation, construction, finishing, etc.), professional tasks (formwork, casting, crane operations, rebar installation, etc.), and even considering time of day, offers the opportunity to formulate a strategic safety plan. Such a plan could entail targeted safety training and specific controls, facilitating thorough risk identification and implementation of an effective control process. In this paper, we present a review of work-accident-related research, categorized into five root causes: Medium, Mission, Man, Management, and Machinery, using the 5M method. This paper suggests a comprehensive methodology that uses the 5M model for investigating work accidents and implements the Analytical Hierarchy Process (AHP) for organizing and analyzing safety resource allocation decisions, in the process of risk management in construction sites, based on structured analysis and probabilistic risk analysis. The proposed model was validated in a case study.