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 (IDRmax) 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 mo...

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Vydané v:	Computers & structures Ročník 289; s. 107181
Hlavní autori:	Asgarkhani, N., Kazemi, F., Jankowski, R.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 01.12.2023
Predmet:	Machine learning algorithm Residual interstory drift Seismic demand Seismic failure probability Seismic risk assessment Soil-structure interaction Soil-structure interaction Seismic risk assessment Residual interstory drift Seismic demand Seismic failure probability Machine learning algorithm
ISSN:	0045-7949, 1879-2243
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Popis
Shrnutí:	•Improved Machine-Learning (ML) techniques were used to predict Maximum Interstory Drift Ratio (IDRmax) 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 (IDAMed) and Seismic Failure Probability curve (SFPCurve) 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 (IDRmax) 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 (IDAMed) and Seismic Failure Probability curve (SFPCurve) 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 IDAMed and SFPCurve 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.
ISSN:	0045-7949 1879-2243
DOI:	10.1016/j.compstruc.2023.107181