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Development of Prediction Model to Predict the Compressive Strength of Eco-Friendly Concrete Using Multivariate Polynomial Regression Combined with Stepwise Method
Concrete is the most widely used building material, but it is also a recognized pollutant, causing significant issues for sustainability in terms of resource depletion, energy use, and greenhouse gas emissions. As a result, efforts should be concentrated on reducing concrete’s environmental conseque...
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/PMC8746230/ https://www.ncbi.nlm.nih.gov/pubmed/35009463 http://dx.doi.org/10.3390/ma15010317 |
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author | Imran, Hamza Al-Abdaly, Nadia Moneem Shamsa, Mohammed Hammodi Shatnawi, Amjed Ibrahim, Majed Ostrowski, Krzysztof Adam |
author_facet | Imran, Hamza Al-Abdaly, Nadia Moneem Shamsa, Mohammed Hammodi Shatnawi, Amjed Ibrahim, Majed Ostrowski, Krzysztof Adam |
author_sort | Imran, Hamza |
collection | PubMed |
description | Concrete is the most widely used building material, but it is also a recognized pollutant, causing significant issues for sustainability in terms of resource depletion, energy use, and greenhouse gas emissions. As a result, efforts should be concentrated on reducing concrete’s environmental consequences in order to increase its long-term viability. In order to design environmentally friendly concrete mixtures, this research intended to create a prediction model for the compressive strength of those mixtures. The concrete mixtures that were used in this study to build our proposed prediction model are concrete mixtures that contain both recycled aggregate concrete (RAC) and ground granulated blast-furnace slag (GGBFS). A white-box machine learning model known as multivariate polynomial regression (MPR) was developed to predict the compressive strength of eco-friendly concrete. The model was compared with the other two machine learning models, where one is also a white-box machine learning model, namely linear regression (LR), and the other is the black-box machine learning model, which is a support vector machine (SVM). The newly suggested model shows robust estimation capabilities and outperforms the other two models in terms of R(2) (coefficient of determination) and RMSE (root mean absolute error) measurements. |
format | Online Article Text |
id | pubmed-8746230 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-87462302022-01-11 Development of Prediction Model to Predict the Compressive Strength of Eco-Friendly Concrete Using Multivariate Polynomial Regression Combined with Stepwise Method Imran, Hamza Al-Abdaly, Nadia Moneem Shamsa, Mohammed Hammodi Shatnawi, Amjed Ibrahim, Majed Ostrowski, Krzysztof Adam Materials (Basel) Article Concrete is the most widely used building material, but it is also a recognized pollutant, causing significant issues for sustainability in terms of resource depletion, energy use, and greenhouse gas emissions. As a result, efforts should be concentrated on reducing concrete’s environmental consequences in order to increase its long-term viability. In order to design environmentally friendly concrete mixtures, this research intended to create a prediction model for the compressive strength of those mixtures. The concrete mixtures that were used in this study to build our proposed prediction model are concrete mixtures that contain both recycled aggregate concrete (RAC) and ground granulated blast-furnace slag (GGBFS). A white-box machine learning model known as multivariate polynomial regression (MPR) was developed to predict the compressive strength of eco-friendly concrete. The model was compared with the other two machine learning models, where one is also a white-box machine learning model, namely linear regression (LR), and the other is the black-box machine learning model, which is a support vector machine (SVM). The newly suggested model shows robust estimation capabilities and outperforms the other two models in terms of R(2) (coefficient of determination) and RMSE (root mean absolute error) measurements. MDPI 2022-01-02 /pmc/articles/PMC8746230/ /pubmed/35009463 http://dx.doi.org/10.3390/ma15010317 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 Imran, Hamza Al-Abdaly, Nadia Moneem Shamsa, Mohammed Hammodi Shatnawi, Amjed Ibrahim, Majed Ostrowski, Krzysztof Adam Development of Prediction Model to Predict the Compressive Strength of Eco-Friendly Concrete Using Multivariate Polynomial Regression Combined with Stepwise Method |
title | Development of Prediction Model to Predict the Compressive Strength of Eco-Friendly Concrete Using Multivariate Polynomial Regression Combined with Stepwise Method |
title_full | Development of Prediction Model to Predict the Compressive Strength of Eco-Friendly Concrete Using Multivariate Polynomial Regression Combined with Stepwise Method |
title_fullStr | Development of Prediction Model to Predict the Compressive Strength of Eco-Friendly Concrete Using Multivariate Polynomial Regression Combined with Stepwise Method |
title_full_unstemmed | Development of Prediction Model to Predict the Compressive Strength of Eco-Friendly Concrete Using Multivariate Polynomial Regression Combined with Stepwise Method |
title_short | Development of Prediction Model to Predict the Compressive Strength of Eco-Friendly Concrete Using Multivariate Polynomial Regression Combined with Stepwise Method |
title_sort | development of prediction model to predict the compressive strength of eco-friendly concrete using multivariate polynomial regression combined with stepwise method |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8746230/ https://www.ncbi.nlm.nih.gov/pubmed/35009463 http://dx.doi.org/10.3390/ma15010317 |
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