Prediction of nonlinear dynamic responses and generation of seismic fragility curves for steel moment frames using boosting machine learning techniques
•Four boosting machine learning (ML) models were developed to predict the seismic responses of steel moment frames.•The maximum global and interstory drift ratios, base shear coefficient, and maximum floor acceleration were predicted.•1,848 steel moment frames were analyzed under 50 earthquake recor...
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| Veröffentlicht in: | Computers & structures Jg. 305; S. 107580 |
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| Sprache: | Englisch |
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01.12.2024
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| ISSN: | 0045-7949 |
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| Abstract | •Four boosting machine learning (ML) models were developed to predict the seismic responses of steel moment frames.•The maximum global and interstory drift ratios, base shear coefficient, and maximum floor acceleration were predicted.•1,848 steel moment frames were analyzed under 50 earthquake records to generate an inclusive dataset.•Fragility curves were estimated using the IDA responses predicted by the LightGBM models.•The LightGBM and CatBoost models achieved the best predictive performance compared to the other models.
The main objective of this paper is to develop machine learning (ML) models for predicting the seismic responses of steel moment frames. For this purpose, four boosting ML techniques-gradient boosting, XGBoost, LightGBM, and CatBoost-were developed in Python. To create an inclusive dataset, 92,400 nonlinear time-history analyses were performed on 1,848 steel moment frames under 50 earthquakes using OpenSeesPy. Geometric configurations, structural properties, and ground motion intensity measures were considered as the inputs for the models. The outputs included maximum global drift ratio (MGDR), maximum interstory drift ratio (MIDR), base shear coefficient (BSC), and maximum floor acceleration (MFA). The study also investigated the effectiveness of the ML models in estimating fragility curves for an 8-story steel frame at different performance levels. Finally, a web application was developed to facilitate the estimation of the peak dynamic responses for steel moment frames. The results show that the LightGBM and CatBoost models demonstrate superior predictive performance, with coefficient of determinations (R2) higher than 0.925. Furthermore, the LightGBM models can estimate the fragility curves with minimal errors (e.g., the relative errors in the median values of the predicted curves are less than 10%). |
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| AbstractList | •Four boosting machine learning (ML) models were developed to predict the seismic responses of steel moment frames.•The maximum global and interstory drift ratios, base shear coefficient, and maximum floor acceleration were predicted.•1,848 steel moment frames were analyzed under 50 earthquake records to generate an inclusive dataset.•Fragility curves were estimated using the IDA responses predicted by the LightGBM models.•The LightGBM and CatBoost models achieved the best predictive performance compared to the other models.
The main objective of this paper is to develop machine learning (ML) models for predicting the seismic responses of steel moment frames. For this purpose, four boosting ML techniques-gradient boosting, XGBoost, LightGBM, and CatBoost-were developed in Python. To create an inclusive dataset, 92,400 nonlinear time-history analyses were performed on 1,848 steel moment frames under 50 earthquakes using OpenSeesPy. Geometric configurations, structural properties, and ground motion intensity measures were considered as the inputs for the models. The outputs included maximum global drift ratio (MGDR), maximum interstory drift ratio (MIDR), base shear coefficient (BSC), and maximum floor acceleration (MFA). The study also investigated the effectiveness of the ML models in estimating fragility curves for an 8-story steel frame at different performance levels. Finally, a web application was developed to facilitate the estimation of the peak dynamic responses for steel moment frames. The results show that the LightGBM and CatBoost models demonstrate superior predictive performance, with coefficient of determinations (R2) higher than 0.925. Furthermore, the LightGBM models can estimate the fragility curves with minimal errors (e.g., the relative errors in the median values of the predicted curves are less than 10%). |
| ArticleNumber | 107580 |
| Author | Zareian, Farzaneh Banazadeh, Mehdi Zareian, Mohammad Sajjad |
| Author_xml | – sequence: 1 givenname: Farzaneh orcidid: 0009-0001-2290-4794 surname: Zareian fullname: Zareian, Farzaneh email: f.zareian@aut.ac.ir organization: Department of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, Iran – sequence: 2 givenname: Mehdi surname: Banazadeh fullname: Banazadeh, Mehdi email: mbanazadeh@aut.ac.ir organization: Department of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, Iran – sequence: 3 givenname: Mohammad Sajjad surname: Zareian fullname: Zareian, Mohammad Sajjad email: zareian@shdu.ac.ir organization: Department of Civil Engineering, Shahab Danesh University, Qom, Iran |
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| Cites_doi | 10.1016/S0141-0296(00)00074-2 10.1016/j.compstruc.2019.03.004 10.1007/s10518-021-01283-x 10.1002/eqe.935 10.1145/2939672.2939785 10.62913/engj.v42i4.863 10.1016/j.engstruct.2022.115502 10.1016/j.ultras.2024.107347 10.1002/eqe.876 10.3390/computation11070126 10.1007/s10518-019-00679-0 10.1061/(ASCE)ST.1943-541X.0003439 10.1193/1.1585416 10.1016/j.engstruct.2018.01.053 10.1016/j.engstruct.2018.02.024 10.3390/infrastructures7040051 10.3390/app12031753 10.1007/s00521-021-06004-8 10.1016/j.engstruct.2021.112518 10.1111/mice.12805 10.1007/s10518-018-0384-y 10.1016/j.engstruct.2005.07.010 10.1016/j.engappai.2023.106976 10.1002/eqe.3183 10.1007/s10518-019-00726-w 10.1007/s00366-022-01609-6 10.1016/0029-5493(90)90259-Z 10.1002/suco.202200718 10.1016/j.advengsoft.2011.05.033 10.1016/S0143-974X(01)00095-5 10.1016/j.soildyn.2020.106096 10.1061/(ASCE)ST.1943-541X.0003004 10.1061/JSENDH.STENG-12969 10.1016/j.eswa.2015.07.053 10.1002/eqe.141 10.1016/j.engstruct.2018.03.028 10.1002/eqe.4290150109 10.1016/j.advengsoft.2020.102825 10.1016/j.soildyn.2004.10.007 10.1016/j.softx.2017.10.009 10.1007/s10518-015-9838-7 10.1061/(ASCE)0733-9445(1991)117:1(19) 10.1007/s10518-016-9963-y 10.1016/j.conbuildmat.2022.129504 10.1061/(ASCE)0733-9445(2002)128:4(429) |
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| Keywords | Nonlinear dynamic responses Fragility curves Steel moment frames Machine learning Boosting algorithms |
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| SubjectTerms | Boosting algorithms Fragility curves Machine learning Nonlinear dynamic responses Steel moment frames |
| Title | Prediction of nonlinear dynamic responses and generation of seismic fragility curves for steel moment frames using boosting machine learning techniques |
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