Integrating Automated Detection Systems and Pre-Construction Optimization in Transportation Infrastructure Projects

Abstract

Transportation infrastructure projects face persistent challenges, including cost overruns, safety risks, and design inaccuracies. This study examines the integration of automated detection systems (e.g., LiDAR, drones) and pre-construction optimization tools (e.g., BIM, GIS) to address these issues. Using a mixed-methods approach, the research analyzed 12 projects and simulated a bridge rehabilitation case to compare conventional, detection-only, and integrated approaches. Findings indicate that the integrated approach reduced cost overruns by 66.7%, safety incidents by 71.4%, and design change orders by 70% compared to conventional methods. Qualitative insights highlighted the importance of training and stakeholder coordination, though barriers like high costs and interoperability issues remain. A framework was developed to guide implementation, emphasizing standardized protocols and financial incentives. While limited to highways and bridges, the study underscores the potential of integrated technologies to enhance project outcomes. Recommendations include subsidies for technology adoption, standardized data formats, and comprehensive training to ensure scalability across diverse infrastructure projects.