During the process of damage identification and safety-state evaluation of cable-stayed bridges, the cable tension should also be incorporated into common monitoring, which usually includes displacement and strain. However, the testing process of cable tension is complicated, and the disassembly, installation and maintenance of the cable tension meter are higher priced and difficult. To improve the efficiency of damage evaluation regarding cable-stayed bridges, information-entropy theory is introduced and the curvature entropy index of the difference in the influence line of displacement is proposed. To obtain effective data parameters for damage evaluation, first, the dynamic disturbance in the displacement time-history response is removed through variational modal decomposition, and the multi-axle effect of vehicles is regularized, so as to identify the measured influence line of displacement of cable-stayed bridges. Second, the peak value of the curvature entropy index of the difference in the influence line of displacement under varied damage degrees of stay cables is extracted to construct the inverse fitting formula of damage degree. The entropy value of the measured influence line of displacement is then substituted into a PSO-BP neural network, so as to obtain the damage degree of the corresponding position of the measured data regarding the influence line of displacement of bridges. Finally, the health status of stay cables is evaluated using the information-entropy parameters of the influence line of displacement. The theoretical model and actual data are used for testing, and the research results show that: (1) the location and degree of cable damage can be effectively located and quantified by using the curvature entropy index of the difference in the influence line of displacement, and (2) the cable health index of the cable-stayed bridge tested by actual data is 96.73%, consistent with the conclusion of on-site technical evaluation.
Zhang et al. (Tue,) studied this question.