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Hybrid Ensemble Model for Predicting the Strength of FRP Laminates Bonded to the Concrete
The goal of this work was to use a hybrid ensemble machine learning approach to estimate the interfacial bond strength (IFB) of fibre-reinforced polymer laminates (FRPL) bonded to the concrete using the results of a single shear-lap test. A database comprising 136 data was used to train and validate...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9460908/ https://www.ncbi.nlm.nih.gov/pubmed/36080580 http://dx.doi.org/10.3390/polym14173505 |
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author | Alabdullh, Anas Abdulalem Biswas, Rahul Gudainiyan, Jitendra Khan, Kaffayatullah Bujbarah, Abdullah Hussain Alabdulwahab, Qasem Ahmed Amin, Muhammad Nasir Iqbal, Mudassir |
author_facet | Alabdullh, Anas Abdulalem Biswas, Rahul Gudainiyan, Jitendra Khan, Kaffayatullah Bujbarah, Abdullah Hussain Alabdulwahab, Qasem Ahmed Amin, Muhammad Nasir Iqbal, Mudassir |
author_sort | Alabdullh, Anas Abdulalem |
collection | PubMed |
description | The goal of this work was to use a hybrid ensemble machine learning approach to estimate the interfacial bond strength (IFB) of fibre-reinforced polymer laminates (FRPL) bonded to the concrete using the results of a single shear-lap test. A database comprising 136 data was used to train and validate six standalone machine learning models, namely, artificial neural network (ANN), extreme machine learning (ELM), the group method of data handling (GMDH), multivariate adaptive regression splines (MARS), least square-support vector machine (LSSVM), and Gaussian process regression (GPR). The hybrid ensemble (HENS) model was subsequently built, employing the combined and trained predicted outputs of the ANN, ELM, GMDH, MARS, LSSVM, and GPR models. In comparison with the standalone models employed in the current investigation, it was observed that the suggested HENS model generated superior predicted accuracy with R(2) (training = 0.9783, testing = 0.9287), VAF (training = 97.83, testing = 92.87), RMSE (training = 0.0300, testing = 0.0613), and MAE (training = 0.0212, testing = 0.0443). Using the training and testing dataset to assess the predictive performance of all models for IFB prediction, it was discovered that the HENS model had the greatest predictive accuracy throughout both stages with an R(2) of 0.9663. According to the findings of the experiments, the newly developed HENS model has a great deal of promise to be a fresh approach to deal with the overfitting problems of CML models and thus may be utilised to forecast the IFB of FRPL. |
format | Online Article Text |
id | pubmed-9460908 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-94609082022-09-10 Hybrid Ensemble Model for Predicting the Strength of FRP Laminates Bonded to the Concrete Alabdullh, Anas Abdulalem Biswas, Rahul Gudainiyan, Jitendra Khan, Kaffayatullah Bujbarah, Abdullah Hussain Alabdulwahab, Qasem Ahmed Amin, Muhammad Nasir Iqbal, Mudassir Polymers (Basel) Article The goal of this work was to use a hybrid ensemble machine learning approach to estimate the interfacial bond strength (IFB) of fibre-reinforced polymer laminates (FRPL) bonded to the concrete using the results of a single shear-lap test. A database comprising 136 data was used to train and validate six standalone machine learning models, namely, artificial neural network (ANN), extreme machine learning (ELM), the group method of data handling (GMDH), multivariate adaptive regression splines (MARS), least square-support vector machine (LSSVM), and Gaussian process regression (GPR). The hybrid ensemble (HENS) model was subsequently built, employing the combined and trained predicted outputs of the ANN, ELM, GMDH, MARS, LSSVM, and GPR models. In comparison with the standalone models employed in the current investigation, it was observed that the suggested HENS model generated superior predicted accuracy with R(2) (training = 0.9783, testing = 0.9287), VAF (training = 97.83, testing = 92.87), RMSE (training = 0.0300, testing = 0.0613), and MAE (training = 0.0212, testing = 0.0443). Using the training and testing dataset to assess the predictive performance of all models for IFB prediction, it was discovered that the HENS model had the greatest predictive accuracy throughout both stages with an R(2) of 0.9663. According to the findings of the experiments, the newly developed HENS model has a great deal of promise to be a fresh approach to deal with the overfitting problems of CML models and thus may be utilised to forecast the IFB of FRPL. MDPI 2022-08-26 /pmc/articles/PMC9460908/ /pubmed/36080580 http://dx.doi.org/10.3390/polym14173505 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Alabdullh, Anas Abdulalem Biswas, Rahul Gudainiyan, Jitendra Khan, Kaffayatullah Bujbarah, Abdullah Hussain Alabdulwahab, Qasem Ahmed Amin, Muhammad Nasir Iqbal, Mudassir Hybrid Ensemble Model for Predicting the Strength of FRP Laminates Bonded to the Concrete |
title | Hybrid Ensemble Model for Predicting the Strength of FRP Laminates Bonded to the Concrete |
title_full | Hybrid Ensemble Model for Predicting the Strength of FRP Laminates Bonded to the Concrete |
title_fullStr | Hybrid Ensemble Model for Predicting the Strength of FRP Laminates Bonded to the Concrete |
title_full_unstemmed | Hybrid Ensemble Model for Predicting the Strength of FRP Laminates Bonded to the Concrete |
title_short | Hybrid Ensemble Model for Predicting the Strength of FRP Laminates Bonded to the Concrete |
title_sort | hybrid ensemble model for predicting the strength of frp laminates bonded to the concrete |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9460908/ https://www.ncbi.nlm.nih.gov/pubmed/36080580 http://dx.doi.org/10.3390/polym14173505 |
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