Prediction of Mechanical Properties of the Stirrup-Confined Rectangular CFST Stub Columns Using FEM and Machine Learning
In this study, a machine learning method using gradient boost regression tree (GBRT) model was presented to predict the ultimate bearing capacity of stirrup-confined rectangular CFST stub columns (SCFST) by using a comprehensive data set and by adjusting the selected parameters indicated in the prev...
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| Format: | Article |
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MDPI AG
2021-07-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/9/14/1643 |
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| author | Deren Lu Zhidong Chen Faxing Ding Zhenming Chen Peng Sun |
| author_facet | Deren Lu Zhidong Chen Faxing Ding Zhenming Chen Peng Sun |
| author_sort | Deren Lu |
| collection | DOAJ |
| description | In this study, a machine learning method using gradient boost regression tree (GBRT) model was presented to predict the ultimate bearing capacity of stirrup-confined rectangular CFST stub columns (SCFST) by using a comprehensive data set and by adjusting the selected parameters indicated in the previous research (<i>B</i>, <i>D</i>, <i>t</i>, <i>ρ</i><sub>sa</sub>, <i>f</i><sub>cu</sub>, <i>f</i><sub>s</sub>). The advantage of GBRT is its strong predictive ability, which can naturally handle different types of data and very robust processing of outliers out of space. The comprehensive data set obtained from the FEM method which has been verified the accuracy and rationality by the existing literature. In order to make the data group closer to the engineering example, a large amount of experimental data collected in the literature was added to the data group to enhance the accuracy of the model. We compare a few regression models simply and the results show that the GBRT model has a good predictive effect on the mechanical properties of CFST columns. In summary, it can help pre-investigations for the CFST columns. |
| first_indexed | 2024-03-10T09:32:40Z |
| format | Article |
| id | doaj.art-b5a154fd80d447ca8ebd17e0b2464199 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T09:32:40Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-b5a154fd80d447ca8ebd17e0b24641992023-11-22T04:19:59ZengMDPI AGMathematics2227-73902021-07-01914164310.3390/math9141643Prediction of Mechanical Properties of the Stirrup-Confined Rectangular CFST Stub Columns Using FEM and Machine LearningDeren Lu0Zhidong Chen1Faxing Ding2Zhenming Chen3Peng Sun4School of Civil Engineering, Central South University, Changsha 410075, ChinaSchool of Civil Engineering, Qinghai University, Xining 810016, ChinaSchool of Civil Engineering, Central South University, Changsha 410075, ChinaChina Construction Science and Industry Corporation Ltd., Shenzhen 518000, ChinaChina Construction Science and Industry Corporation Ltd., Shenzhen 518000, ChinaIn this study, a machine learning method using gradient boost regression tree (GBRT) model was presented to predict the ultimate bearing capacity of stirrup-confined rectangular CFST stub columns (SCFST) by using a comprehensive data set and by adjusting the selected parameters indicated in the previous research (<i>B</i>, <i>D</i>, <i>t</i>, <i>ρ</i><sub>sa</sub>, <i>f</i><sub>cu</sub>, <i>f</i><sub>s</sub>). The advantage of GBRT is its strong predictive ability, which can naturally handle different types of data and very robust processing of outliers out of space. The comprehensive data set obtained from the FEM method which has been verified the accuracy and rationality by the existing literature. In order to make the data group closer to the engineering example, a large amount of experimental data collected in the literature was added to the data group to enhance the accuracy of the model. We compare a few regression models simply and the results show that the GBRT model has a good predictive effect on the mechanical properties of CFST columns. In summary, it can help pre-investigations for the CFST columns.https://www.mdpi.com/2227-7390/9/14/1643machine learning methodgradient boost regression tree (GBRT) modelstirrup-confined rectangular CFST stub columnsfinite element analysesprediction |
| spellingShingle | Deren Lu Zhidong Chen Faxing Ding Zhenming Chen Peng Sun Prediction of Mechanical Properties of the Stirrup-Confined Rectangular CFST Stub Columns Using FEM and Machine Learning Mathematics machine learning method gradient boost regression tree (GBRT) model stirrup-confined rectangular CFST stub columns finite element analyses prediction |
| title | Prediction of Mechanical Properties of the Stirrup-Confined Rectangular CFST Stub Columns Using FEM and Machine Learning |
| title_full | Prediction of Mechanical Properties of the Stirrup-Confined Rectangular CFST Stub Columns Using FEM and Machine Learning |
| title_fullStr | Prediction of Mechanical Properties of the Stirrup-Confined Rectangular CFST Stub Columns Using FEM and Machine Learning |
| title_full_unstemmed | Prediction of Mechanical Properties of the Stirrup-Confined Rectangular CFST Stub Columns Using FEM and Machine Learning |
| title_short | Prediction of Mechanical Properties of the Stirrup-Confined Rectangular CFST Stub Columns Using FEM and Machine Learning |
| title_sort | prediction of mechanical properties of the stirrup confined rectangular cfst stub columns using fem and machine learning |
| topic | machine learning method gradient boost regression tree (GBRT) model stirrup-confined rectangular CFST stub columns finite element analyses prediction |
| url | https://www.mdpi.com/2227-7390/9/14/1643 |
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