A structural health monitoring (SHM) system provides an efficient way to diagnose the condition of critical and large-scale structures such as long-span bridges. With the development of SHM techniques, numerous condition assessment and damage diagnosis methods have been developed to monitor the evolution of deterioration and long-term structural performance of such structures, as well as to conduct rapid damage and post-disaster assessments. However, the condition assessment and the damage detection methods described in the literature are usually validated by numerical simulation and/or laboratory testing of small-scale structures with assumed deterioration models and artificial damage, which makes the comparison of different methods invalid and unconvincing to a certain extent. This paper presents a full-scale bridge benchmark problem organized by the Center of Structural Monitoring and Control at the Harbin Institute of Technology. The benchmark bridge structure, the SHM system, the finite element model of the bridge, and the monitored data are presented in detail. Focusing on two critical and vulnerable components of cable-stayed bridges, two benchmark problems are proposed on the basis of the field monitoring data from the full-scale bridge, that is, condition assessment of stay cables (Benchmark Problem 1) and damage detection of bridge girders (Benchmark Problem 2). For Benchmark Problem 1, the monitored cable stresses and the fatigue properties of the deteriorated steel wires and cables are presented. The fatigue life prediction model and the residual fatigue life assessment of the cables are the foci of this problem. For Benchmark Problem 2, several damage patterns were observed for the cable-stayed bridge. The acceleration time histories, together with the environmental conditions during the damage development process of the bridge, are provided. Researchers are encouraged to detect and to localize the damage and the damage development process. All the datasets and detailed descriptions, including the cable stresses, the acceleration datasets, and the finite element model, are available on the Structural Monitoring and Control website (http://smc.hit.edu.cn). Copyright © 2013 John Wiley & Sons, Ltd.

With the structural health monitoring technique, several medium- and small-span bridges possessing the same or similar structural characteristics located in a local road network are simultaneously monitored within one cluster. Nevertheless, insufficient research has been performed on detecting the damage of all the bridges monitored within one cluster under time-varying environmental temperatures. To address this issue, a method is proposed to assess the damage in all the bridges within one cluster utilizing the residual between the cumulative distribution functions of strain monitoring data. First, an algorithm for reconstructing the strain monitoring dataset is introduced, effectively improving the computational efficiency at removing as much measured noise from a large amount of monitoring data as possible. Second, a cluster analysis algorithm considering the similarity among the strain monitoring data at different measured points is proposed to classify the strain monitoring data for all the bridges monitored within one cluster. Different classes of strain monitoring data are established by identifying similar probability distribution patterns. Third, for each class, a damage detection index is proposed based on the residual between the cumulative distribution functions of strain monitoring data. All the bridges monitored within one cluster are acted upon by similar environmental temperatures during the same monitoring period; hence, the effects of the environmental temperature on the proposed index are mitigated indirectly during the same monitoring period. Thus, the proposed index is implemented to effectively detect the damage in all the bridges belonging to each class. Finally, the effectiveness of the proposed method is demonstrated through a numerical simulation and with strain monitoring data obtained from actual bridges.