A response-compatible ground motion generation method using physics-guided neural networks

Abstract

Selecting or generating ground motions (GMs) that elicit seismic responses matching specific standards or expected benchmarks for nonlinear time-history analysis (NLTHA) is crucial for ensuring the rationality of structural seismic design and analysis. Typical GM inputs for NLTHA, either natural or artificial, are normally spectrum-compatible, which may produce significant variations in analysis results, even using multiple GMs. This paper introduces a response-compatible ground motion generation (RCGMG) method for generating GMs that are tailored to be response-compatible. NLTHA results using only a few of these artificial GMs can closely approximate the mean responses from a large set of natural spectrum-compatible GMs or target responses. The RCGMG method adopts the response diagram in the time domain (RDTD) to characterize the nonstationary features of GMs and their impacts on structural dynamic responses. A physics-guided conditional generative adversarial network is developed to produce artificial RDTDs with features and impacts of RDTDs of natural GMs. These generated RDTDs are then mapped into response-compatible GMs through a feedforward neural network. To verify the effectiveness of RCGMG, NLTHA of different structure models under various site conditions and target spectra is conducted. Seismic responses of NLTHA using RCGMG-generated GMs are compared with responses from spectrum-compatible natural GMs. The results demonstrate that responses from RCGMG GMs are closer to the target responses, with fewer GM inputs and robust generalization performance.