It is well-known that a bridge may end up with severe damage under aftershocks, even if it resisted the main shock. As such, a quick assessment seems necessary when a bridge experiences an earthquake event. In this paper, an output-only energy-based method was developed to locate damaged cables in an tied-arch bridge under seismic excitation. For this purpose, seismic acceleration response signals were first subjected to simple band-pass filtering. Then, the energy levels, in terms of Arias intensity, were calculated and normalized to locate the damaged cables. The proposed method was validated through a realistic 3-D numerical model of a tied-arch bridge under 16 different ground motions. It was shown that the proposed method can accurately detect the damaged cables under the earthquakes. Moreover, the damage detection procedure is proved to be insensitive to noise and adequately robust against near-and far-field parameters of the earthquake. KEYWORDSseismic acceleration response, Arias intensity, band-pass filtering, detection of damaged cable, output-only method, tied-arch bridge Struct Control Health Monit. 2020;27:e2491.wileyonlinelibrary.com/journal/stc

Rapid and accurate assessment of the damage to bridge structures after an earthquake can provide a basis for decision-making regarding post-earthquake emergency work. However, the traditional structural damage inspection techniques are subjective, time-consuming, and inefficient. This paper proposed a framework for rapid post-earthquake structural damage inspection and condition assessment by integrating the technologies of satellite, unmanned aerial vehicle (UAV), and smartphone with the deep learning approach. The images of structural components of post-earthquake bridges can be obtained by UAVs and smartphones. Furthermore, the multi-task high-resolution net (MT-HRNet) model was adopted to recognize the structural components and damage conditions by weighting and combining the loss functions of a single-task HRNet model. The performance of the proposed MT-HRNet model and the single-task HRNet model was verified based on the Tokaido dataset, which includes 2000 images of post-earthquake bridges. The results showed that the MT-HRNet model and the HRNet model exhibited equivalent recognition accuracy, while the number of floating-point-operations (FLOPs) and the parameters of the MT-HRNet model were reduced by 46.48% and 49.58% compared with the HRNet model. In addition, a method for the determination of the safety risk level of the post-earthquake bridge structures was developed, and the evaluation indices were established by considering the damage type, the spalling area, and the width of cracks as well as the recognition statistics of all images in Tokaido dataset. This study will provide a valuable reference for the rapid determination of structural safety level and the corresponding treatment measures of post-earthquake bridges.

The interesting to assess the condition of a structure with structural health monitoring data has gained many attentions. Most of the existing methods require the measurement at the force location. This paper presents a novel output-only condition assessment method that does not require measurement at the force location. The unknown structural damage indices and input force can be identified with the stochastic gradient descent method. The dynamic acceleration response sensitivities with respect to the unknown structural damage indices and input force are derived analytically. Both unknown damage indices and unknown input force can be identified by minimizing the discrepancy between the measured and calculated vibration data. Numerical studies on a two-dimensional truss and seven-floor frame and experimental studies on a steel frame structure are presented to verify the accuracy and efficiency of the proposed method. Results demonstrate that the damage severity, location, and unknown input force can be identified. Also, the measurement at the force location is not required. Furthermore, when 20% measurement noise is considered, the identified error is less than 5%.

No abstract