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Evaluating enhanced predictive modeling of foam concrete compressive strength using artificial intelligence algorithms

Artificial intelligence algorithms have recently demonstrated their efficacy in accurately predicting concrete properties by optimizing mixing proportions and overcoming design limitations. In this regard, foam concrete (FC) production presents a unique challenge, necessitating extensive experimenta...

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Main Authors:	Abdellatief M., Wong L.S., Din N.M., Mo K.H., Ahmed A.N., El-Shafie A.
Other Authors:	57855303900
Format:	Article
Published:	Elsevier Ltd 2025
Subjects:	Cements Concrete aggregates Concrete mixing Forecasting Learning algorithms Machine learning Mean square error Multilayer neural networks Parameter estimation Regression analysis Sensitivity analysis Artificial intelligence algorithms Compressive strength prediction- parametric analyse Foam concretes Gaussian process regression Input variables Machine learning algorithms Parametric analysis Regression algorithms Strength prediction Support vector regressions Compressive strength
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id	my.uniten.dspace-36426
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spelling	my.uniten.dspace-364262025-03-03T15:42:22Z Evaluating enhanced predictive modeling of foam concrete compressive strength using artificial intelligence algorithms Abdellatief M. Wong L.S. Din N.M. Mo K.H. Ahmed A.N. El-Shafie A. 57855303900 55504782500 9335429400 55915884700 57214837520 16068189400 Cements Concrete aggregates Concrete mixing Forecasting Learning algorithms Machine learning Mean square error Multilayer neural networks Parameter estimation Regression analysis Sensitivity analysis Artificial intelligence algorithms Compressive strength prediction- parametric analyse Foam concretes Gaussian process regression Input variables Machine learning algorithms Parametric analysis Regression algorithms Strength prediction Support vector regressions Compressive strength Artificial intelligence algorithms have recently demonstrated their efficacy in accurately predicting concrete properties by optimizing mixing proportions and overcoming design limitations. In this regard, foam concrete (FC) production presents a unique challenge, necessitating extensive experimental trials to attain specific properties such as compressive strength (CS). In this context, linear regression (LR), support vector regression (SVR), a multilayer-perceptron artificial neural network (MLP-ANN), and Gaussian process regression (GPR) algorithms, were used to predict the CS of FC. 261 experimental results were utilized, incorporating input variables such as density, water-to-cement ratio, and fine aggregate-to-cement ratio. During the training phase, 75 % of the experimental dataset was utilized. The experimental data is then validated using metrics such as coefficient of determination (R2), root mean square error, and root mean error. In comparison, the GPR algorithm reveals high-accuracy towards the estimation of CS, as proved by its high R2-value, which equals 0.98, while the R2 for ANN, SVR, and LR are 0.97, 0.90, and 0.89, respectively. Additionally, parametric and sensitivity analyses were used to assess the performance of the GPR and LR algorithms. Results revealed that density exerted the most significant influence on CS, with the GPR model showing a pronounced negative impact of fine aggregate-to-cement ratio on CS, particularly in low-density FC, contrasting with the LR model. This study confirmed that the GPR algorithm provided reliable accuracy in predicting the CS of FC. Therefore, it is recommended to utilize the prediction algorithms within the range of input variables employed in this investigation for optimal results. ? 2024 Elsevier Ltd Final 2025-03-03T07:42:22Z 2025-03-03T07:42:22Z 2024 Article 10.1016/j.mtcomm.2024.110022 2-s2.0-85200420453 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200420453&doi=10.1016%2fj.mtcomm.2024.110022&partnerID=40&md5=26d447b78dba66730a6a0118cdf92048 https://irepository.uniten.edu.my/handle/123456789/36426 40 110022 Elsevier Ltd Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
topic	Cements Concrete aggregates Concrete mixing Forecasting Learning algorithms Machine learning Mean square error Multilayer neural networks Parameter estimation Regression analysis Sensitivity analysis Artificial intelligence algorithms Compressive strength prediction- parametric analyse Foam concretes Gaussian process regression Input variables Machine learning algorithms Parametric analysis Regression algorithms Strength prediction Support vector regressions Compressive strength
spellingShingle	Cements Concrete aggregates Concrete mixing Forecasting Learning algorithms Machine learning Mean square error Multilayer neural networks Parameter estimation Regression analysis Sensitivity analysis Artificial intelligence algorithms Compressive strength prediction- parametric analyse Foam concretes Gaussian process regression Input variables Machine learning algorithms Parametric analysis Regression algorithms Strength prediction Support vector regressions Compressive strength Abdellatief M. Wong L.S. Din N.M. Mo K.H. Ahmed A.N. El-Shafie A. Evaluating enhanced predictive modeling of foam concrete compressive strength using artificial intelligence algorithms
description	Artificial intelligence algorithms have recently demonstrated their efficacy in accurately predicting concrete properties by optimizing mixing proportions and overcoming design limitations. In this regard, foam concrete (FC) production presents a unique challenge, necessitating extensive experimental trials to attain specific properties such as compressive strength (CS). In this context, linear regression (LR), support vector regression (SVR), a multilayer-perceptron artificial neural network (MLP-ANN), and Gaussian process regression (GPR) algorithms, were used to predict the CS of FC. 261 experimental results were utilized, incorporating input variables such as density, water-to-cement ratio, and fine aggregate-to-cement ratio. During the training phase, 75 % of the experimental dataset was utilized. The experimental data is then validated using metrics such as coefficient of determination (R2), root mean square error, and root mean error. In comparison, the GPR algorithm reveals high-accuracy towards the estimation of CS, as proved by its high R2-value, which equals 0.98, while the R2 for ANN, SVR, and LR are 0.97, 0.90, and 0.89, respectively. Additionally, parametric and sensitivity analyses were used to assess the performance of the GPR and LR algorithms. Results revealed that density exerted the most significant influence on CS, with the GPR model showing a pronounced negative impact of fine aggregate-to-cement ratio on CS, particularly in low-density FC, contrasting with the LR model. This study confirmed that the GPR algorithm provided reliable accuracy in predicting the CS of FC. Therefore, it is recommended to utilize the prediction algorithms within the range of input variables employed in this investigation for optimal results. ? 2024 Elsevier Ltd
author2	57855303900
author_facet	57855303900 Abdellatief M. Wong L.S. Din N.M. Mo K.H. Ahmed A.N. El-Shafie A.
format	Article
author	Abdellatief M. Wong L.S. Din N.M. Mo K.H. Ahmed A.N. El-Shafie A.
author_sort	Abdellatief M.
title	Evaluating enhanced predictive modeling of foam concrete compressive strength using artificial intelligence algorithms
title_short	Evaluating enhanced predictive modeling of foam concrete compressive strength using artificial intelligence algorithms
title_full	Evaluating enhanced predictive modeling of foam concrete compressive strength using artificial intelligence algorithms
title_fullStr	Evaluating enhanced predictive modeling of foam concrete compressive strength using artificial intelligence algorithms
title_full_unstemmed	Evaluating enhanced predictive modeling of foam concrete compressive strength using artificial intelligence algorithms
title_sort	evaluating enhanced predictive modeling of foam concrete compressive strength using artificial intelligence algorithms
publisher	Elsevier Ltd
publishDate	2025
_version_	1825816181657829376
score	13.244109

Evaluating enhanced predictive modeling of foam concrete compressive strength using artificial intelligence algorithms

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