Abstract

Preventive maintenance efforts for bridge infrastructure have proven to mitigate early deterioration and reduce the probability of severe damage. Modern research has focused on the employment of online data directly collected within the structures, provided by several novel devices that feed machine learning approaches that continuously measure structural health. However, several issues remain within the related fields. The constant evolution of ML techniques, for example, provides new potential lines of research. Furthermore, widespread validation through real-world test cases and the use of diverse bridge typologies (which can be interesting considering their distinct behaviors) remain limited. This article seeks to examine the advancements in structural health monitoring (SHM) employing machine learning methods for identifying structural damage in bridges over a 7-year period, with a particular focus on studies employing real bridge data. Present challenges and future research directions are assessed. This study offers valuable insights to researchers and academics conducting research in this field, providing a thorough summary of current developments and combining ML methods with the four most-investigated bridge types in case studies.