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Multigene Expression Programming Based Forecasting the Hardened Properties of Sustainable Bagasse Ash Concrete
The application of multiphysics models and soft computing techniques is gaining enormous attention in the construction sector due to the development of various types of concrete. In this research, an improved form of supervised machine learning, i.e., multigene expression programming (MEP), has been...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8510050/ https://www.ncbi.nlm.nih.gov/pubmed/34640055 http://dx.doi.org/10.3390/ma14195659 |
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author | Amin, Muhammad Nasir Khan, Kaffayatullah Aslam, Fahid Shah, Muhammad Izhar Javed, Muhammad Faisal Musarat, Muhammad Ali Usanova, Kseniia |
author_facet | Amin, Muhammad Nasir Khan, Kaffayatullah Aslam, Fahid Shah, Muhammad Izhar Javed, Muhammad Faisal Musarat, Muhammad Ali Usanova, Kseniia |
author_sort | Amin, Muhammad Nasir |
collection | PubMed |
description | The application of multiphysics models and soft computing techniques is gaining enormous attention in the construction sector due to the development of various types of concrete. In this research, an improved form of supervised machine learning, i.e., multigene expression programming (MEP), has been used to propose models for the compressive strength ([Formula: see text]), splitting tensile strength ([Formula: see text]), and flexural strength ([Formula: see text]) of sustainable bagasse ash concrete (BAC). The training and testing of the proposed models have been accomplished by developing a reliable and comprehensive database from published literature. Concrete specimens with varying proportions of sugarcane bagasse ash (BA), as a partial replacement of cement, were prepared, and the developed models were validated by utilizing the results obtained from the tested BAC. Different statistical tests evaluated the accurateness of the models, and the results were cross-validated employing a k-fold algorithm. The modeling results achieve correlation coefficient (R) and Nash-Sutcliffe efficiency (NSE) above 0.8 each with relative root mean squared error (RRMSE) and objective function (OF) less than 10 and 0.2, respectively. The MEP model leads in providing reliable mathematical expression for the estimation of [Formula: see text] , [Formula: see text] and [Formula: see text] of BA concrete, which can reduce the experimental workload in assessing the strength properties. The study’s findings indicated that MEP-based modeling integrated with experimental testing of BA concrete and further cross-validation is effective in predicting the strength parameters of BA concrete. |
format | Online Article Text |
id | pubmed-8510050 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-85100502021-10-13 Multigene Expression Programming Based Forecasting the Hardened Properties of Sustainable Bagasse Ash Concrete Amin, Muhammad Nasir Khan, Kaffayatullah Aslam, Fahid Shah, Muhammad Izhar Javed, Muhammad Faisal Musarat, Muhammad Ali Usanova, Kseniia Materials (Basel) Article The application of multiphysics models and soft computing techniques is gaining enormous attention in the construction sector due to the development of various types of concrete. In this research, an improved form of supervised machine learning, i.e., multigene expression programming (MEP), has been used to propose models for the compressive strength ([Formula: see text]), splitting tensile strength ([Formula: see text]), and flexural strength ([Formula: see text]) of sustainable bagasse ash concrete (BAC). The training and testing of the proposed models have been accomplished by developing a reliable and comprehensive database from published literature. Concrete specimens with varying proportions of sugarcane bagasse ash (BA), as a partial replacement of cement, were prepared, and the developed models were validated by utilizing the results obtained from the tested BAC. Different statistical tests evaluated the accurateness of the models, and the results were cross-validated employing a k-fold algorithm. The modeling results achieve correlation coefficient (R) and Nash-Sutcliffe efficiency (NSE) above 0.8 each with relative root mean squared error (RRMSE) and objective function (OF) less than 10 and 0.2, respectively. The MEP model leads in providing reliable mathematical expression for the estimation of [Formula: see text] , [Formula: see text] and [Formula: see text] of BA concrete, which can reduce the experimental workload in assessing the strength properties. The study’s findings indicated that MEP-based modeling integrated with experimental testing of BA concrete and further cross-validation is effective in predicting the strength parameters of BA concrete. MDPI 2021-09-28 /pmc/articles/PMC8510050/ /pubmed/34640055 http://dx.doi.org/10.3390/ma14195659 Text en © 2021 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 Amin, Muhammad Nasir Khan, Kaffayatullah Aslam, Fahid Shah, Muhammad Izhar Javed, Muhammad Faisal Musarat, Muhammad Ali Usanova, Kseniia Multigene Expression Programming Based Forecasting the Hardened Properties of Sustainable Bagasse Ash Concrete |
title | Multigene Expression Programming Based Forecasting the Hardened Properties of Sustainable Bagasse Ash Concrete |
title_full | Multigene Expression Programming Based Forecasting the Hardened Properties of Sustainable Bagasse Ash Concrete |
title_fullStr | Multigene Expression Programming Based Forecasting the Hardened Properties of Sustainable Bagasse Ash Concrete |
title_full_unstemmed | Multigene Expression Programming Based Forecasting the Hardened Properties of Sustainable Bagasse Ash Concrete |
title_short | Multigene Expression Programming Based Forecasting the Hardened Properties of Sustainable Bagasse Ash Concrete |
title_sort | multigene expression programming based forecasting the hardened properties of sustainable bagasse ash concrete |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8510050/ https://www.ncbi.nlm.nih.gov/pubmed/34640055 http://dx.doi.org/10.3390/ma14195659 |
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