Machine learning-empowered intelligent vehicle-bridge systems: Current status and future prospects

Bridges are critical links for transportation networks, making their functionality and serviceability attract longlasting research interests. Among various loads acting on bridges during their lifetime, vehicle/traffic loads are recognized as the most dominant, which excites extensive studies to understand the complex behavior of vehicle-bridge systems (VBS). Traditional methods for analyzing VBS face significant challenges due to the increasing demand for accuracy, efficiency, and adaptability. Recent advancements in machine learning (ML) offer promising solutions to these challenges, bearing great potential to develop intelligent vehicle-bridge systems (IVBS) that are imperative for future intelligent monitoring and maintenance of bridges. This paper reviews the current status of ML applications in VBS, highlighting how ML enhances vehicle load monitoring, bridge dynamic performance and reliability evaluation, and bridge damage identification. This paper also discusses the key challenges and associated countermeasures of integrating ML into VBS, attempting to provide the first seminal roadmap for building future IVBS.