Machine learning-based prediction of seismic limit-state capacity of steel moment-resisting frames considering soil-structure interaction

Regarding the unpredictable and complex nature of seismic excitations, there is a need for vulnerability assessment of newly constructed or existing structures. Predicting the seismic limit-state capacity of steel Moment-Resisting Frames (MRFs) can help designers to have a preliminary estimation and improve their views about the seismic performance of the designed structure. This study improved data-driven decision techniques in Python software, known as supervised Machine Learning (ML) algorithms, to find median IDA curves (M-IDAs) for predicting the seismic limit-state capacities of steel MRFs considering Soil-Structure Interaction (SSI) effects. For this purpose, Incremental Dynamic Analyses (IDAs) were performed on the steel MRFs from two to nine-story elevations modeled in Opensees subjected to three ground motion subsets of Far Fault (FF), near-fault Pulse-Like (PL) and No-Pulse (NP) suggested by FEMA-P695. The result of the analysis confirmed that there is no specific model for predicting the MIDA curve of steel structures; therefore, the best developed ML algorithms to reduce a complex modeling process with high computational cost using 128,000 data points were proposed. To provide convenient access to prediction results, Graphical User Interface (GUI) was developed to predict Sa(T1) of seismic limit-state performance levels with a large database based on prediction models. (c) 2022 Elsevier Ltd. All rights reserved.

Automated summary

Due to the unpredictable and complex nature of seismic excitations, vulnerability assessment of structures is necessary. This study utilized supervised Machine Learning (ML) algorithms in Python software to improve data-driven decision techniques and predict the seismic limit-state capacities of steel Moment-Resisting Frames (MRFs) considering Soil-Structure Interaction (SSI) effects. Incremental Dynamic Analyses (IDAs) were performed on steel MRFs of different heights subjected to various ground motion subsets, and the best ML algorithms were proposed to reduce the modeling process complexity. A Graphical User Interface (GUI) was developed to provide convenient access to prediction results based on a large database. (c) 2022 Elsevier Ltd. All rights reserved.