Hybrid data-driven and physics-based simulation technique for seismic response evaluation of steel buckling-restrained braced frames considering brace fracture

This paper proposes a hybrid data-driven and physics-based simulation technique for seismic response evaluation of steel Buckling-Restrained Braced Frames (BRBFs) considering brace fracture. Buckling-Restrained Brace (BRB) fracture is represented by cumulative plastic deformation capacity. A dataset, consisting of 95 past BRB laboratory tests and 120 simulated BRB responses generated using the finite element method, is first developed. An Artificial Neural Network-based (ANN) predictive model is then trained using the training dataset to estimate the cumulative plastic deformation of BRBs. The prediction capability of the ANN-based predictive model is validated using the training dataset and an existing regression-based predictive model. In the second part of the paper, an hybrid simulation technique combining the data-driven model and physics-based numerical modeling is presented to conduct the nonlinear time history analysis, followed by 1) validation against a full-scale BRBF testing and 2) demonstration of the proposed simulation technique using a six-story BRBF. The results confirm that the proposed predictive model can predict the BRB fracture with sufficient accuracy. Furthermore, the hybrid data-driven physics-based simulation technique can be used as a powerful tool for dynamic analysis of BRBFs considering BRB fracture.