The Use of Machine Learning Models in Estimating the Compressive Strength of Recycled Brick Aggregate Concrete

The focus of this study is to investigate the applicability of Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN), and Multiple Linear Regression (MLR) in modeling the compressive strength of Recycled Brick Aggregate Concrete (RBAC). A comparative study on the application...

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Autores principales:	Atefehossadat Khademi, Kiachehr Behfarnia, Tanja Kalman Šipoš, Ivana Miličević
Formato:	article
Lenguaje:	EN
Publicado:	Pouyan Press 2021
Materias:	concrete cement compressive strength anfis artificial neural network regression Computer engineering. Computer hardware TK7885-7895
Acceso en línea:	https://doaj.org/article/9369efb9ae0e42238aced75ff08b4687
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spelling	oai:doaj.org-article:9369efb9ae0e42238aced75ff08b46872021-12-03T15:27:45ZThe Use of Machine Learning Models in Estimating the Compressive Strength of Recycled Brick Aggregate Concrete2588-695910.22115/cepm.2021.297016.1181https://doaj.org/article/9369efb9ae0e42238aced75ff08b46872021-10-01T00:00:00Zhttp://www.jcepm.com/article_136944_9225826aecc887623dcecdf80b592eea.pdfhttps://doaj.org/toc/2588-6959The focus of this study is to investigate the applicability of Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN), and Multiple Linear Regression (MLR) in modeling the compressive strength of Recycled Brick Aggregate Concrete (RBAC). A comparative study on the application of the aforementioned models is developed based on statistical tools such as coefficient of determination, mean absolute error, root mean squared error, and some others, and the application potential of each of these models is investigated. To study the effects of RBAC factors on the performance of representative data-driven models, the Sensitivity Analysis (SA) method is used. The findings revealed that ANN with R2 value of 0.9102 has a great application potential in predicting the compressive strength of concrete. In the absence of ANN, ANFIS with R2 value of 0.8538 is also an excellent substitute for predictions. MLR was shown to be less effective than the preceding models and is only recommended for preliminary estimations. In addition, Subsequent sensitivity analysis on the database indicates the reliability of the prediction models have a strong correlation to the number of input parameters. The application of ANN and ANFIS as a precursor to traditional methods can eliminate the need for old-style tests, thus, constituting a significant reduction in time and expense needed for design and/or repairs.Atefehossadat KhademiKiachehr BehfarniaTanja Kalman ŠipošIvana MiličevićPouyan Pressarticleconcretecementcompressive strengthanfisartificial neural networkregressionComputer engineering. Computer hardwareTK7885-7895ENComputational Engineering and Physical Modeling, Vol 4, Iss 4, Pp 1-25 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	concrete cement compressive strength anfis artificial neural network regression Computer engineering. Computer hardware TK7885-7895
spellingShingle	concrete cement compressive strength anfis artificial neural network regression Computer engineering. Computer hardware TK7885-7895 Atefehossadat Khademi Kiachehr Behfarnia Tanja Kalman Šipoš Ivana Miličević The Use of Machine Learning Models in Estimating the Compressive Strength of Recycled Brick Aggregate Concrete
description	The focus of this study is to investigate the applicability of Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN), and Multiple Linear Regression (MLR) in modeling the compressive strength of Recycled Brick Aggregate Concrete (RBAC). A comparative study on the application of the aforementioned models is developed based on statistical tools such as coefficient of determination, mean absolute error, root mean squared error, and some others, and the application potential of each of these models is investigated. To study the effects of RBAC factors on the performance of representative data-driven models, the Sensitivity Analysis (SA) method is used. The findings revealed that ANN with R2 value of 0.9102 has a great application potential in predicting the compressive strength of concrete. In the absence of ANN, ANFIS with R2 value of 0.8538 is also an excellent substitute for predictions. MLR was shown to be less effective than the preceding models and is only recommended for preliminary estimations. In addition, Subsequent sensitivity analysis on the database indicates the reliability of the prediction models have a strong correlation to the number of input parameters. The application of ANN and ANFIS as a precursor to traditional methods can eliminate the need for old-style tests, thus, constituting a significant reduction in time and expense needed for design and/or repairs.
format	article
author	Atefehossadat Khademi Kiachehr Behfarnia Tanja Kalman Šipoš Ivana Miličević
author_facet	Atefehossadat Khademi Kiachehr Behfarnia Tanja Kalman Šipoš Ivana Miličević
author_sort	Atefehossadat Khademi
title	The Use of Machine Learning Models in Estimating the Compressive Strength of Recycled Brick Aggregate Concrete
title_short	The Use of Machine Learning Models in Estimating the Compressive Strength of Recycled Brick Aggregate Concrete
title_full	The Use of Machine Learning Models in Estimating the Compressive Strength of Recycled Brick Aggregate Concrete
title_fullStr	The Use of Machine Learning Models in Estimating the Compressive Strength of Recycled Brick Aggregate Concrete
title_full_unstemmed	The Use of Machine Learning Models in Estimating the Compressive Strength of Recycled Brick Aggregate Concrete
title_sort	use of machine learning models in estimating the compressive strength of recycled brick aggregate concrete
publisher	Pouyan Press
publishDate	2021
url	https://doaj.org/article/9369efb9ae0e42238aced75ff08b4687
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The Use of Machine Learning Models in Estimating the Compressive Strength of Recycled Brick Aggregate Concrete

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