Integration of safety risk data with highway construction schedules

Abstract

The construction industry is characterized by a relatively high injury and illness rate compared to other industries. Within the construction industry, the highway construction and maintenance sector is one of the most dangerous. To improve safety in this sector, proactive methods of safety improvement and reliable risk data are needed. The safety risk quantification is the first step towards integrating safety data into design and planning. To enhance the current preconstruction safety practices, safety risks of highway construction and maintenance tasks were quantified and a decision support system was developed and tested that integrates safety risk data into the project schedules. Relative safety risks were quantified for 25 common highway construction tasks using the Delphi method. To ensure valid and reliable results, experts were selected according to rigorous requirements and multiple controls were employed to decrease cognitive biases. The data were incorporated into a decision support system called Scheduled-based Safety Risk Assessment and Management (SSRAM) that facilitates integration of safety risk data with project schedules. The resulting data-driven system produces predictive plots of safety risk over time based on the temporal and spatial interactions among concurrent activities. To test the utility of the decision support system and the validity of the underlying risk data, the system was tested on 11 active case study projects in the US. It was found that the database and associated decision support tool produce accurate and reliable risk forecasts that increase the viability of existing safety preconstruction activities.

Keywords:

• Decision support systems
• occupational health and safety
• risk management
• scheduling

Acknowledgements

The writers would like to thank Bentley Systems for the resources and high quality feedback during the project and all of the Delphi panellists for their enthusiastic participation.