Enhancing Real-Time Data Acquisition from Embedded Road Structural Health Monitoring Systems

Abstract

Canada's economy heavily relies on its roadways, yet managing pavement assets faces challenges due to past infrastructure spending cuts. Addressing this, a pavement management system (PMS) is essential for efficient resource allocation. Traditional surface condition monitoring within PMS is time-consuming and costly. In response, in situ condition monitoring, integrating AI and ML, emerges as a viable alternative, aligning with the development of "smart" pavements. This thesis, part of the Smart Pavements project at the University of Waterloo, assesses an instrumented pavement section on Courtland Avenue, Kitchener. It provides a comprehensive assessment of the implementation, data collection, data analysis and database concept development processes of an instrumented test section. It integrates advanced monitoring technologies and predictive modeling, yielding promising results. Identified gaps in the literature are addressed, with scalability and cost-benefit analysis highlighted for future research. Challenges in instrumentation and testing, including weather delays, are discussed, with recommendations provided. Material testing procedures and truck testing results are outlined, emphasizing seasonal variations and the impact of vehicle wander on pavement behavior. Software comparisons and detailed trend analysis reveal insights into pavement performance. Additionally, a basic database framework is proposed for efficient data management. This study contributes to understanding pavement instrumentation, long-term behavior, and the efficacy of simulation methods. Recommendations for future work include AI/ML integration, long-term data collection, database development, and standardized guidelines for sensor implementation, aiming to enhance pavement management practices nationwide.