TransFrameNet: A transformer-based approach for generalized seismic performance prediction of building structures

Abstract

Performing nonlinear seismic analysis on a large number of building structures is challenging. Deep learning offers rapid prediction but still with limitations. One model is generally only applicable to a specific building structure and not easily extended to others. To address this, TransFrameNet, a new method based on Transformer, is proposed. By converting buildings into archetypes, TransFrameNet is able to consider the variations of different buildings in geometric features and component sizes. With hard parameter sharing, multi-task learning further expands the applications for multiple building structures with different designs. TransFrameNet is tested on 100 steel moment resisting frames (SMRFs) to predict floor displacement response using 40 seismic ground motions. Results reveal that TransFrameNet can accurately predict the displacement response of different buildings, with an average mean squared error of 0.0037. Notably, compared to LSTM and Transformer models, TransFrameNet shows significantly improved correlation when tested on a 20-story SMRF structure.