State‐of‐the‐art review of vibration‐based bridge health monitoring using Artificial Intelligence

Abstract Due to the deterioration and aging of bridge structures over the past decades, structural health monitoring (SHM) systems have garnered significant attention from researchers worldwide. SHM systems encompass multiple modules, including sensing, data collection, transmission, management, damage detection, and safety assessment. As a highly interdisciplinary field, SHM integrates various technologies such as sensor sensing, data acquisition, signal processing, and optimization. One of the promising approaches in bridge health monitoring (BHM) is vibration‐based monitoring, which provides critical information for bridge condition assessment and maintenance. In recent years, advancements in computer hardware and Artificial Intelligence (AI) algorithms have significantly enhanced the capability of vibration‐based BHM systems. AI, with its advanced analytical power and high sensitivity to anomalies, has been widely adopted in these applications, enabling more efficient and accurate damage detection. This paper presents a state‐of‐the‐art review of vibration‐based BHM using various AI techniques over the past two years. It explores how AI can facilitate data‐driven BHM systems for bridges and discusses key aspects of the BHM process, including existing methodologies and current challenges. Additionally, the paper highlights potential research directions to guide future studies, offering insights and opportunities for researchers in the field.