Damage detection of bridges monitored within one cluster based on the residual between the cumulative distribution functions of strain monitoring data

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.