Using leading and lagging indicators to select safe contractors at the prequalification stage of construction projects.

The contractor-selection decision at the prequalification stage is critical to the project success. An insufficient prediction of contractors' safety capacities using only lagging indicators may hinder the continuous improvement of safety performance in the construction industry. This research enhanced construction management and practices by proposing a comprehensive safe contractor selection model which integrated both leading and lagging indicators. First, a set of leading and lagging safety indicators were identified based on literature review and expert opinions. Then, the grey correlation analysis (GCA) was utilized to assign weights to individual indicators. We found that management commitment, safety training and education, safety risk management, and safety rules and procedures were four most influential factors to the safety performance of contractors. In addition, the fuzzy technique of ordering preference by similarity to ideal solution (Fuzzy TOPSIS) was used to condense individual indicators and create a composite safety performance indicator (c-SPI). Finally, the feasibility of the decision support tool for safe contractor selection was verified using a real-case railway construction project.