Development of data-driven models to predict seismic drift response of RC wall structures: An application of deep neural networks

Abstract

This research aimed to develop data-driven models using deep neural networks (DNNs) that can rapidly predict the seismic drift responses of reinforced concrete (RC) wall structures in building frame systems, aiming to overcome the challenges of costly seismic performance evaluation of building structures. A total of 46 RC wall structures ranging from four to 40 stories were analyzed and subjected to 1,000 ground motions including farfield, near-field-pulse, and near-field-no-pulse types. Two input sets were investigated initially. The first input set comprises peak ground acceleration (PGA), spectral accelerations at 1 s-6 s with 1-s intervals, and the first five natural periods of the wall structure. The second input set contains PGA and spectral accelerations at the first five natural periods of the wall structure. The DNN model developed based on the first input set demonstrated superior accuracy achieving an R2 value of 0.880, and slightly outperformed other reputable machine learning models. To enhance the applicability of the developed model, the ground motion type was incorporated as an additional variable to the first input set, which led to an R2 value of 0.869. Ultimately, two DNN models, one based on the first input set and the other considering the ground motion type, were proposed in this study. Finally, the two models were applied to quickly predict seismic drift responses of a new 24-story RC wall structure, and the predicted results were then utilized to efficiently construct the fragility function. The findings highlight the potential of DNNs as an efficient solution to address complex and costly challenges in the earthquake engineering domain.