Attention-based hybrid network for structural nonlinear response prediction under long-period earthquake

To develop structures that can respond to severe seismic events, models and tools are essential for quickly and precisely assessing and controlling the structure's performance. Long-period ground motions may pose potentially significant hazards to the safety of high-rise buildings located in basins or alluvial plains. However, traditional regression-based methods and pure data-driven machine learning methods are insufficient to address this problem of response prediction, which is characterized by non-stationarity, nonlinearity, and scarcity of samples. To tackle this issue, we propose a novel semi-explicit physics-informed deep learning method. The method incorporates the physical information into the model, thereby achieving better prediction accuracy. Through numerical and experimental validations, including hysteresis experimental data, the actual seismic response record of a six-story hotel, as well as data from single-degree-offreedom structures across various periods and multi-story buildings in Los Angeles, Seattle, and Boston, we verified the proposed method significantly outperforms traditional regression-based methods and data-driven machine learning methods in terms of accuracy, efficiency, and robustness.