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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...

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Bibliographic Details
Main Author: Tien-Thinh Le
Format: Bài trích
Language:eng
Published: Advances in Civil Engineering 2021
Online Access:https://www.hindawi.com/journals/ace/2020/8832522/
https://dlib.phenikaa-uni.edu.vn/handle/PNK/2841
https://doi.org/10.1155/2020/8832522
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spelling oai:localhost:PNK-28412022-08-17T05:54:47Z Practical Hybrid Machine Learning Approach for Estimation of Ultimate Load of Elliptical Concrete-Filled Steel Tubular Columns under Axial Loading Tien-Thinh Le 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. 2021-09-14T07:14:52Z 2021-09-14T07:14:52Z 2021 Bài trích https://www.hindawi.com/journals/ace/2020/8832522/ https://dlib.phenikaa-uni.edu.vn/handle/PNK/2841 https://doi.org/10.1155/2020/8832522 eng application/pdf Advances in Civil Engineering
institution Digital Phenikaa
collection Digital Phenikaa
language eng
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 Bài trích
author Tien-Thinh Le
spellingShingle Tien-Thinh Le
Practical Hybrid Machine Learning Approach for Estimation of Ultimate Load of Elliptical Concrete-Filled Steel Tubular Columns under Axial Loading
author_facet Tien-Thinh Le
author_sort Tien-Thinh Le
title 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_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_sort practical hybrid machine learning approach for estimation of ultimate load of elliptical concrete-filled steel tubular columns under axial loading
publisher Advances in Civil Engineering
publishDate 2021
url https://www.hindawi.com/journals/ace/2020/8832522/
https://dlib.phenikaa-uni.edu.vn/handle/PNK/2841
https://doi.org/10.1155/2020/8832522
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score 8.887929

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