Seismic response and performance prediction of steel buckling-restrained braced frames using machine-learning methods

Nowadays, Buckling-Restrained Brace Frames (BRBFs) have been used as lateral force-resisting systems for low-, to mid-rise buildings. Residual Interstory Drift (RID) of BRBFs plays a key role in deciding to retrofit buildings after seismic excitation; however, existing formulas have limitations and cannot effectively help civil engineers, e.g., FEMA P-58, which is a conservative estimation method. Therefore, there is a need to provide a comprehensive tool for estimating seismic responses of Interstory Drift (ID) and RID with novel approaches to fulfill the shortcomings of existing formulas. The Machine Learning (ML) method is an interdisciplinary approach that makes it possible to solve these types of engineering problems. Therefore, the current study proposes ML algorithms to provide a prediction model for determining the seismic response, seismic performance curve, and seismic failure probability curve of BRBFs. To train ML-based prediction models, Nonlinear Time-History Analysis (NTHA) and Incremental Dynamic Analysis (IDA) were performed on the 2-, to 12-Story BRBFs subjected to 78 far-field ground motions, and 606944 data points were prepared for different prediction purposes. The results indicate that the considered approaches are justified. For instance, the proposed ML methods have the ability to predict the maximum ID, maximum RID and maximum roof ID with the accuracy of even 98.7%, 95.2%, and 93.8%, respectively, for the 4-Story BRBF. Moreover, a general preliminary estimation tool is introduced to provide predictions based on the input parameters considered in the study.