Practical Hybrid Machine Learning Approach for Estimation of Ultimate Load of Elliptical Concrete-Filled Steel Tubular Columns under Axial Loading
In this study, a hybrid machine learning (ML) technique was proposed to predict the bearing capacity of elliptical CFST columns under axial load. The proposed model was Adaptive Neurofuzzy Inference System (ANFIS) combined with Real Coded Genetic Algorithm (RCGA), denoted as RCGA-ANFIS. The evaluati...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2020/8832522 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832546884021911552 |
|---|---|
| author | Tien-Thinh Le |
| author_facet | Tien-Thinh Le |
| author_sort | Tien-Thinh Le |
| collection | DOAJ |
| description | In this study, a hybrid machine learning (ML) technique was proposed to predict the bearing capacity of elliptical CFST columns under axial load. The proposed model was Adaptive Neurofuzzy Inference System (ANFIS) combined with Real Coded Genetic Algorithm (RCGA), denoted as RCGA-ANFIS. The evaluation of the model was performed using the coefficient of determination (R2) and root mean square error (RMSE). The results showed that the RCGA-ANFIS (R2 = 0.974) was more reliable and effective than conventional gradient descent (GD) technique (R2 = 0.952). The accuracy of the present work was found superior to the results published in the literature (R2 = 0.776 or 0.768) when predicting the load capacity of elliptical CFST columns. Finally, sensitivity analysis showed that the thickness of the steel tube and the minor axis length of the elliptical cross section were the most influential parameters. For practical application, a Graphical User Interface (GUI) was developed in MATLAB for researchers and engineers and to support the teaching and interpretation of the axial behavior of CFST columns. |
| format | Article |
| id | doaj-art-7ad0f5933a36422294329933a938ebfa |
| institution | Kabale University |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-7ad0f5933a36422294329933a938ebfa2025-02-03T06:46:47ZengWileyAdvances in Civil Engineering1687-80861687-80942020-01-01202010.1155/2020/88325228832522Practical Hybrid Machine Learning Approach for Estimation of Ultimate Load of Elliptical Concrete-Filled Steel Tubular Columns under Axial LoadingTien-Thinh Le0Faculty of Mechanical Engineering and Mechatronics, Phenikaa University, Yen Nghia, Ha Dong, Hanoi 12116, VietnamIn this study, a hybrid machine learning (ML) technique was proposed to predict the bearing capacity of elliptical CFST columns under axial load. The proposed model was Adaptive Neurofuzzy Inference System (ANFIS) combined with Real Coded Genetic Algorithm (RCGA), denoted as RCGA-ANFIS. The evaluation of the model was performed using the coefficient of determination (R2) and root mean square error (RMSE). The results showed that the RCGA-ANFIS (R2 = 0.974) was more reliable and effective than conventional gradient descent (GD) technique (R2 = 0.952). The accuracy of the present work was found superior to the results published in the literature (R2 = 0.776 or 0.768) when predicting the load capacity of elliptical CFST columns. Finally, sensitivity analysis showed that the thickness of the steel tube and the minor axis length of the elliptical cross section were the most influential parameters. For practical application, a Graphical User Interface (GUI) was developed in MATLAB for researchers and engineers and to support the teaching and interpretation of the axial behavior of CFST columns.http://dx.doi.org/10.1155/2020/8832522 |
| spellingShingle | Tien-Thinh Le Practical Hybrid Machine Learning Approach for Estimation of Ultimate Load of Elliptical Concrete-Filled Steel Tubular Columns under Axial Loading Advances in Civil Engineering |
| title | Practical Hybrid Machine Learning Approach for Estimation of Ultimate Load of Elliptical Concrete-Filled Steel Tubular Columns under Axial Loading |
| title_full | Practical Hybrid Machine Learning Approach for Estimation of Ultimate Load of Elliptical Concrete-Filled Steel Tubular Columns under Axial Loading |
| title_fullStr | Practical Hybrid Machine Learning Approach for Estimation of Ultimate Load of Elliptical Concrete-Filled Steel Tubular Columns under Axial Loading |
| title_full_unstemmed | Practical Hybrid Machine Learning Approach for Estimation of Ultimate Load of Elliptical Concrete-Filled Steel Tubular Columns under Axial Loading |
| title_short | Practical Hybrid Machine Learning Approach for Estimation of Ultimate Load of Elliptical Concrete-Filled Steel Tubular Columns under Axial Loading |
| title_sort | practical hybrid machine learning approach for estimation of ultimate load of elliptical concrete filled steel tubular columns under axial loading |
| url | http://dx.doi.org/10.1155/2020/8832522 |
| work_keys_str_mv | AT tienthinhle practicalhybridmachinelearningapproachforestimationofultimateloadofellipticalconcretefilledsteeltubularcolumnsunderaxialloading |