Machine learning-based prediction of residual drift and seismic risk assessment of steel moment-resisting frames considering soil-structure interaction.

• Improved Machine-Learning (ML) techniques were used to predict Maximum Interstory Drift Ratio (IDR max) and Residual Interstory Drift Ratio (RIDR) of 384 Special Moment-Resisting Frames (SMRFs) considering Soil-Structure Interaction (SSI). • Developed ML algorithms were trained to build a surrogate model for seismic vulnerability and risk assessment of SMRFs. • The performance indicators showed that prediction models had higher performance with acceptable accuracy compared to actual values. • The curve plot ability of methods for estimating Median of IDA curve (IDA Med) and Seismic Failure Probability curve (SFP Curve) was confirmed. Nowadays, due to improvements in seismic codes and computational devices, retrofitting buildings is an important topic, in which, permanent deformation of buildings, known as Residual Interstory Drift Ratio (RIDR), plays a crucial role. To provide an accurate yet reliable prediction model, 32 improved Machine Learning (ML) algorithms were considered using the Python software to investigate the best method for estimating Maximum Interstory Drift Ratio (IDR max) and RIDR of 384 Steel Moment-Resisting Frames (SMRFs). In addition, the curve plot ability of methods was investigated to provide an estimation of Median of IDA curve (IDA Med) and Seismic Failure Probability curve (SFP Curve) considering Soil-Structure Interaction (SSI) effects. It is noteworthy that ML algorithms were improved with a pipeline-based hyper-parameters Fine-Tuning (FT) method followed by forward and backward feature selection methodologies to avoid overfitting and data leakage issues. The improved methods were evaluated to find the best prediction model regarding seismic demands. The results show that proposed methods have higher prediction accuracy and curve fitting ability (i.e. more than 95%) that can be used to estimate IDA Med and SFP Curve of a structure to accelerate the seismic risk assessment. A prediction tool is introduced to use the methods of this study for estimating abovementioned seismic demands. [ABSTRACT FROM AUTHOR]