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A Comparative Study for the Prediction of the Compressive Strength of Self-Compacting Concrete Modified with Fly Ash
Artificial intelligence and machine learning are employed in creating functions for the prediction of self-compacting concrete (SCC) strength based on input variables proportion as cement replacement. SCC incorporating waste material has been used in learning approaches. Artificial neural network (A...
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/PMC8434412/ https://www.ncbi.nlm.nih.gov/pubmed/34501024 http://dx.doi.org/10.3390/ma14174934 |
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author | Farooq, Furqan Czarnecki, Slawomir Niewiadomski, Pawel Aslam, Fahid Alabduljabbar, Hisham Ostrowski, Krzysztof Adam Śliwa-Wieczorek, Klaudia Nowobilski, Tomasz Malazdrewicz, Seweryn |
author_facet | Farooq, Furqan Czarnecki, Slawomir Niewiadomski, Pawel Aslam, Fahid Alabduljabbar, Hisham Ostrowski, Krzysztof Adam Śliwa-Wieczorek, Klaudia Nowobilski, Tomasz Malazdrewicz, Seweryn |
author_sort | Farooq, Furqan |
collection | PubMed |
description | Artificial intelligence and machine learning are employed in creating functions for the prediction of self-compacting concrete (SCC) strength based on input variables proportion as cement replacement. SCC incorporating waste material has been used in learning approaches. Artificial neural network (ANN) support vector machine (SVM) and gene expression programming (GEP) consisting of 300 datasets have been utilized in the model to foresee the mechanical property of SCC. Data used in modeling consist of several input parameters such as cement, water–binder ratio, coarse aggregate, fine aggregate, and fly ash (FA) in combination with the superplasticizer. The best predictive models were selected based on the coefficient of determination (R(2)) results and model validation. Empirical relation with mathematical expression has been proposed using ANN, SVM, and GEP. The efficiency of the models is assessed by permutation features importance, statistical analysis, and comparison between regression models. The results reveal that the proposed machine learning models achieved adamant accuracy and has elucidated performance in the prediction aspect. |
format | Online Article Text |
id | pubmed-8434412 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-84344122021-09-12 A Comparative Study for the Prediction of the Compressive Strength of Self-Compacting Concrete Modified with Fly Ash Farooq, Furqan Czarnecki, Slawomir Niewiadomski, Pawel Aslam, Fahid Alabduljabbar, Hisham Ostrowski, Krzysztof Adam Śliwa-Wieczorek, Klaudia Nowobilski, Tomasz Malazdrewicz, Seweryn Materials (Basel) Article Artificial intelligence and machine learning are employed in creating functions for the prediction of self-compacting concrete (SCC) strength based on input variables proportion as cement replacement. SCC incorporating waste material has been used in learning approaches. Artificial neural network (ANN) support vector machine (SVM) and gene expression programming (GEP) consisting of 300 datasets have been utilized in the model to foresee the mechanical property of SCC. Data used in modeling consist of several input parameters such as cement, water–binder ratio, coarse aggregate, fine aggregate, and fly ash (FA) in combination with the superplasticizer. The best predictive models were selected based on the coefficient of determination (R(2)) results and model validation. Empirical relation with mathematical expression has been proposed using ANN, SVM, and GEP. The efficiency of the models is assessed by permutation features importance, statistical analysis, and comparison between regression models. The results reveal that the proposed machine learning models achieved adamant accuracy and has elucidated performance in the prediction aspect. MDPI 2021-08-30 /pmc/articles/PMC8434412/ /pubmed/34501024 http://dx.doi.org/10.3390/ma14174934 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 Farooq, Furqan Czarnecki, Slawomir Niewiadomski, Pawel Aslam, Fahid Alabduljabbar, Hisham Ostrowski, Krzysztof Adam Śliwa-Wieczorek, Klaudia Nowobilski, Tomasz Malazdrewicz, Seweryn A Comparative Study for the Prediction of the Compressive Strength of Self-Compacting Concrete Modified with Fly Ash |
title | A Comparative Study for the Prediction of the Compressive Strength of Self-Compacting Concrete Modified with Fly Ash |
title_full | A Comparative Study for the Prediction of the Compressive Strength of Self-Compacting Concrete Modified with Fly Ash |
title_fullStr | A Comparative Study for the Prediction of the Compressive Strength of Self-Compacting Concrete Modified with Fly Ash |
title_full_unstemmed | A Comparative Study for the Prediction of the Compressive Strength of Self-Compacting Concrete Modified with Fly Ash |
title_short | A Comparative Study for the Prediction of the Compressive Strength of Self-Compacting Concrete Modified with Fly Ash |
title_sort | comparative study for the prediction of the compressive strength of self-compacting concrete modified with fly ash |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8434412/ https://www.ncbi.nlm.nih.gov/pubmed/34501024 http://dx.doi.org/10.3390/ma14174934 |
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