基于物理信息引导深度学习的建筑响应实时预测方法

High-precision and efficient structural response prediction is essential for intelligent disaster prevention and mitigation in building structures,including post-earthquake damage assessment,structural health monitoring,and seismic resilience assessment of buildings.To improve the accuracy and efficiency of structural response prediction,this study proposes a novel physics-informed deep-learning-based realtime structural response prediction method that can predict a large number of nodes in a structure through a data-driven training method and an autoregressive training strategy.The proposed method includes a Phy-Seisformer model that incorporates the physical information of the structure into the model,thereby enabling higher-precision predictions.Experiments were conducted on a four-story masonry structure,an eleven-story reinforced concrete irregular structure,and a twenty-one-story reinforced concrete frame structure to verify the accuracy and efficiency of the proposed method.In addition,the effectiveness of the structure in the Phy-Seisformer model was verified using an ablation study.Furthermore,by conducting a comparative experiment,the impact of the range of seismic wave amplitudes on the prediction accuracy was studied.The experimental results show that the method proposed in this paper can achieve very high accuracy and at least 5000 times faster calculation speed than finite element calculations for different types of building structures.

Intensity measures for seismic liquefaction hazard evaluation of sloping site

This work investigates the correlation between a large number of widely used ground motion intensity measures(IMs) and the corresponding liquefaction potential of a soil deposit during earthquake loading. In order to accomplish this purpose the seismic responses of 32 sloping liquefiable site models consisting of layered cohesionless soil were subjected to 139 earthquake ground motions. Two sets of ground motions, consisting of 80 ordinary records and 59 pulse-like near-fault records are used in the dynamic analyses. The liquefaction potential of the site is expressed in terms of the the mean pore pressure ratio, the maximum ground settlement, the maximum ground horizontal displacement and the maximum ground horizontal acceleration. For each individual accelerogram, the values of the aforementioned liquefaction potential measures are determined. Then, the correlation between the liquefaction potential measures and the IMs is evaluated. The results reveal that the velocity spectrum intensity(VSI) shows the strongest correlation with the liquefaction potential of sloping site. VSI is also proven to be a sufficient intensity measure with respect to earthquake magnitude and source-to-site distance, and has a good predictability, thus making it a prime candidate for the seismic liquefaction hazard evaluation.