Detecting Multiple Change Points in Distributional Sequences Derived from Structural Health Monitoring Data: An Application to Bridge Damage Detection

Detecting damage in critical structures using monitored data is a fundamental task of structural health monitoring, which is extremely important for maintaining structures' safety and life-cycle management. Based on statistical pattern recognition paradigm, damage detection can be conducted by assessing changes in the distribution of properly extracted damage-sensitive features (DSFs). This can be naturally formulated as a distributional change-point detection problem. A good change-point detector for damage detection should be scalable to large DSF datasets, applicable to different types of changes, and capable of controlling for false-positive indications. This study proposes a new distributional change-point detection method for damage detection to address these challenges. We embed the elements of a DSF distributional sequence into the Wasserstein space and construct a moving sum (MOSUM) multiple change-point detector based on Fr\'echet statistics and establish theoretical properties. Extensive simulation studies demonstrate the superiority of our proposed approach against other competitors to address the aforementioned practical requirements. We apply our method to the cable-tension measurements monitored from a long-span cable-stayed bridge for cable damage detection. We conduct a comprehensive change-point analysis for the extracted DSF data, and reveal interesting patterns from the detected changes, which provides valuable insights into cable system damage.