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Detecting damage in important structures using monitored data is afundamental task of structural health monitoring, which is very important forthe structures' safety and life-cycle management. Based on the statisticalpattern recognition paradigm, damage detection can be achieved by detectingchanges in distribution of properly extracted damage-sensitive features (DSFs).This can be naturally formulated as a distributional change-point detectionproblem. A good change-point detector for damage detection should be scalableto large DSF datasets, applicable to different types of changes and able tocontrol the false-positive indication rate. To address these challenges, wepropose a new distributional change-point detection method for damagedetection. We embed the elements of a DSF distributional sequence into theWasserstein space and develop a MOSUM-type multiple change-point detector basedon Fr\'echet statistics. Theoretical properties are also established. Extensivesimulation studies demonstrate the superiority of our proposal against othercompetitors in addressing the aforementioned practical requirements. We applyour method to the cable-tension measurements monitored from a long-spancable-stayed bridge for cable damage detection. We conduct a comprehensivechange-point analysis for the extracted DSF data, and find some interestingpatterns from the detected changes, which provides important insights into thedamage of the cable system.

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