Structural damage assessment after earthquakes using time-frequency analysis and deep learning
To assess the damage state of reinforced concrete(RC)frame structures after earth-quakes and improve the efficiency and accuracy of damage assessment,this study proposes an earthquake damage assessment method based on time-frequency analysis and one-dimensional convolutional neural network(1D-CNN).First,the earthquake damage to a six-story RC frame structure was simulated using incremental dynamic analysis.Based on the maximum story drift ratio,the degree of damage was calibrated to obtain data samples.Second,four different time-frequency analysis methods were applied to process the original signals.Third,an earthquake damage assessment model based on a 1D-CNN was established,and the optimal parameter com-bination in the model was determined using the Bayesian optimization algorithm.Finally,the generalization ability of the proposed model under noise was evaluated.The results show that among five time-frequency analysis methods,the wavelet-scattering transform method has the highest accuracy,reaching 92.5％,and the fastest calculation speed,taking only 144 s.In addi-tion,the proposed method can maintain a high level of damage assessment accuracy under noise conditions,indicating good robustness and generalization ability.
国家科技期刊平台
NSTL
万方数据
维普
