Machine learning-based prediction of seismic limit-state capacity of steel moment-resisting frames considering soil-structure interaction. (1st January 2023)

• Record Type: Journal Article
• Authors: Kazemi, F.; Jankowski, R.
• Is Part Of: Computers & structures. Volume 274(2022)
• Publication Date: 2023-01-01
• Subjects: Machine learning algorithm -- Data-driven decision techniques -- Supervised learning -- Soil-structure interaction -- Seismic vulnerability assessment -- Seismic limit-state capacity; Structural engineering -- Data processing -- Periodicals; Electronic data processing -- Structures, Theory of -- Periodicals; 624.171
• DOI: 10.1016/j.compstruc.2022.106886
• Languages: English
• ISSNs: 0045-7949

Abstract

Highlights: Improved data-driven decision techniques performed to predict seismic limit-state capacities of steel Moment-Resisting Frames (MRFs) founded on different soil types. Supervised machine learning algorithms implemented in Python software are trained to build a surrogate model of steel MRFs considering soil-structure interaction. Graphical User Interface (GUI) was developed to predict Sa ( T 1 ) for the seismic limit-state performance levels of steel MRFs. The developed GUI mitigates the need for computationally expensive, time-consuming, and complex analysis, while provides median IDA curve of steel MRFs excluding and including SSI effects. Abstract: 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 M-IDA 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 ( T 1 ) of seismic limit-state performance levels with a large database based on prediction models.