As essential components of the modern urban system, the health conditions of civil structures are the foundation of urban system sustainability and need to be continuously monitored. In Structural Health Monitoring (SHM), many existing works will have limited performance in the sequential damage diagnosis process because 1) the damage events needs to be reported with short delay, 2) multiple damage locations have to be identified simultaneously, and 3) the computational complexity is intractable in large-scale wireless sensor networks (WSNs). To address these drawbacks, we propose a new damage identification approach that utilizes the time-series of damage sensitive features extracted from multiple sensors' measurements and the optimal change point detection theory to find damage occurrence time and identify the number of damage locations. As the existing change point detection methods require to centralize the sensor data, which is impracticable in many applications, we use the probabilistic graphical model to formulate WSNs and the targeting structure and propose a distributed algorithm for structural damage identification. Validation results show highly accurate damage identification in a shake table experiment and American Society of Civil Engineers benchmark structure. Also, we demonstrate that the detection delay is reduced significantly by utilizing multiple sensors' data.