Damage detection for structural health monitoring using reinforcement and imitation learning

Abstract

Structural damages are responsible for expenses associated with maintaining the safety and serviceability of infrastructures. Detecting damages is difficult because they often develop over years affecting structural responses in orders of magnitudes smaller than external effects, such as temperature. When damage occurs, structural responses depart from a normal condition to an abnormal one, which is referred to as an anomaly. Existing anomaly detection methodologies lack a mechanism to quantify the probability of rightfully detecting anomalies as a function of the anomaly’s characteristics, e.g. duration and magnitude, and associate them with the severity of structural damages. This paper proposes a framework addressing these challenges by relying on Bayesian dynamic linear models as well as reinforcement and imitation learning approaches. The former allows separating the changes in the structural responses from the ones caused by external effects, while the latter two enable incorporating information obtained from the changes in the structural responses for detecting anomalies. The proposed methodologies are validated using measurements collected on three instrumented bridge spans in Canada. The results show a good performance of the methods proposed in detecting structural damages with different severity levels and lay the foundation for further applications for other civil infrastructures.