Physics-informed Temporal Convolutional Network with Bidirectional Gated Recurrent Unit developed for rapid prediction of seismic response time-histories of building structures

Rapid and accurate prediction of structural response histories across multiple seismic hazard levels is essential for seismic performance and resilience evaluation. While surrogate modeling for peak values of individual responses has emerged as a popular solution, existing approaches often struggle to predict time-history-related quantities, particularly permanent residual displacements, due to highly nonlinear hysteretic behaviors during earthquakes. To address this, this study presents a surrogate model development framework for predicting seismic response time-histories using a physics-informed temporal convolutional network integrated with bidirectional gated recurrent units, referred to as PhyTCN-BiGRU . Through enforcing physical consistency through equation-of-motion constraints, the proposed framework effectively captures complex path-dependent dynamics. Validated on a Bouc-Wen system and a nonlinear four-story steel frame, the model demonstrates superior accuracy in predicting full time-history responses, including peak and residual demands, under varying seismic intensities. PhyTCN-BiGRU demonstrates strong capability in reproducing nonlinear seismic response time-histories under earthquakes with both low and high intensities. Critical engineering demand parameters (EDPs) are predicted with exceptional accuracy. The results confirm that PhyTCN-BiGRU offers a reliable and computationally efficient surrogate for seismic response history prediction, including peak and residual values, which can be further used for performance-based seismic assessment and design.