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As essential components of the modern urban system, the health conditions ofcivil structures are the foundation of urban system sustainability and need tobe continuously monitored. In Structural Health Monitoring (SHM), many existingworks will have limited performance in the sequential damage diagnosis processbecause 1) the damage events needs to be reported with short delay, 2) multipledamage locations have to be identified simultaneously, and 3) the computationalcomplexity is intractable in large-scale wireless sensor networks (WSNs). Toaddress these drawbacks, we propose a new damage identification approach thatutilizes the time-series of damage sensitive features extracted from multiplesensors' measurements and the optimal change point detection theory to finddamage occurrence time and identify the number of damage locations. As theexisting change point detection methods require to centralize the sensor data,which is impracticable in many applications, we use the probabilistic graphicalmodel to formulate WSNs and the targeting structure and propose a distributedalgorithm for structural damage identification. Validation results show highlyaccurate damage identification in a shake table experiment and American Societyof Civil Engineers benchmark structure. Also, we demonstrate that the detectiondelay is reduced significantly by utilizing multiple sensors' data.

Sequential Multiple Structural Damage Detection and Localization: A Distributed Approach