Most regional seismic damage assessment (RSDA) methods are based on the rigid-base assumption to ensure evaluating efficiency, while these practices introduce factual errors due to neglecting the soil–structure interaction (SSI). Predicting the influence of the SSI on seismic responses of regionwide structure portfolios remains a challenging undertaking, as it requires developing numerous high-fidelity, integrated models to capture the dynamic interplay and uncertainties in structures, foundations, and supporting soils. This study develops a one-dimensional convolutional neural network (1D-CNN) model to efficiently predict to what degree considering the SSI would change the inter-story drifts and base shear forces of RC frame buildings. An experimentally validated finite element model is developed to simulate the nonlinear seismic behavior of the building-foundation–soil system. Subsequently, a database comprising input data (i.e., structural and soil parameters, ground motions) and output predictors (i.e., changes in story drift and base shear) is constructed by simulating 1380 pairs of fixed-base versus soil-supported structures under earthquake loading. This large-scale dataset is used to train, test, and identify the optimal hyperparameters for the 1D-CNN model to quantify the demand differences in inter-story drifts and base shears due to the SSI. Results indicate the 1D-CNN model has a superior performance, and the absolute prediction errors of the SSI influence coefficients for the maximum base shear and inter-story drift are within 9.3% and 11.7% for 80% of cases in the testing set. The deep learning model can be conveniently applied to enhance the accuracy of the RSDA of RC buildings by updating their seismic responses where no SSI is considered.

This paper presents a performance-based seismic design method of RC moment resisting frame with friction damped self-centering tension braces (FSTB-Frame). The structural configuration and theoretical model of FSTB is introduced, and the numerical model of FSTB is established and validated through cyclic loading tests of a FSTB specimen. Based on the equivalent linear method, the seismic design procedure is developed and verified through 3-, 6-, 9-, and 12-story structures with different design performance objectives. Numerical models of FSTB-frames are established and used to verify whether the design performance targets are achieved through nonlinear dynamic analyses using the design-basis earthquake (DBE) ground motions. The research results show FSTB-frames can achieve the intended performance levels in terms of story drift, residual story drift and base shear, and the maximum and residual story drift of RC frames can be significantly decreased after equipping FSTBs.