A Study on the Prediction of Compressive Strength of Self-Compacting Recycled Aggregate Concrete Utilizing Novel Computational Approaches

A considerable amount of discarded building materials are produced each year worldwide, resulting in ecosystem degradation. Self-compacting concrete (SCC) has 60–70% coarse and fine particles in its composition, so replacing this material with another waste material, such as recycled aggregate (RA),...

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Published in:	Materials Vol. 15; no. 15; p. 5232
Main Authors:	de-Prado-Gil, Jesús, Palencia, Covadonga, Jagadesh, P., Martínez-García, Rebeca
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 28.07.2022 MDPI
Subjects:	Accuracy Aggregates Algorithms Artificial intelligence Artificial neural networks Back propagation Back propagation networks Brain Building materials Compressive strength Concrete aggregates Construction materials Correlation coefficients Deep learning Machine learning Mathematical functions Neural networks Recycled materials Regularization Self-compacting concrete Sensitivity analysis Variables compressive strength artificial neural network self-compacting concrete recycled aggregates Bayesian regularization Levenberg–Marquardt Scaled Conjugate Gradient Backpropagation
ISSN:	1996-1944, 1996-1944
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Abstract	A considerable amount of discarded building materials are produced each year worldwide, resulting in ecosystem degradation. Self-compacting concrete (SCC) has 60–70% coarse and fine particles in its composition, so replacing this material with another waste material, such as recycled aggregate (RA), reduces the cost of SCC. This study compares novel Artificial Neural Network algorithm techniques—Levenberg–Marquardt (LM), Bayesian regularization (BR), and Scaled Conjugate Gradient Backpropagation (SCGB)—to estimate the 28-day compressive strength (f’c) of SCC with RA. A total of 515 samples were collected from various published papers, randomly splitting into training, validation, and testing with percentages of 70, 10 and 20. Two statistical indicators, correlation coefficient (R) and mean squared error (MSE), were used to assess the models; the greater the R and lower the MSE, the more accurate the algorithm. The findings demonstrate the higher accuracy of the three models. The best result is achieved by BR (R = 0.91 and MSE = 43.755), while the accuracy of LM is nearly the same (R = 0.90 and MSE = 48.14). LM processes the network in a much shorter time than BR. As a result, LM and BR are the best models in forecasting the 28 days f’c of SCC having RA. The sensitivity analysis showed that cement (28.39%) and water (23.47%) are the most critical variables for predicting the 28-day compressive strength of SCC with RA, while coarse aggregate contributes the least (9.23%).
AbstractList	A considerable amount of discarded building materials are produced each year worldwide, resulting in ecosystem degradation. Self-compacting concrete (SCC) has 60–70% coarse and fine particles in its composition, so replacing this material with another waste material, such as recycled aggregate (RA), reduces the cost of SCC. This study compares novel Artificial Neural Network algorithm techniques—Levenberg–Marquardt (LM), Bayesian regularization (BR), and Scaled Conjugate Gradient Backpropagation (SCGB)—to estimate the 28-day compressive strength (f’c) of SCC with RA. A total of 515 samples were collected from various published papers, randomly splitting into training, validation, and testing with percentages of 70, 10 and 20. Two statistical indicators, correlation coefficient (R) and mean squared error (MSE), were used to assess the models; the greater the R and lower the MSE, the more accurate the algorithm. The findings demonstrate the higher accuracy of the three models. The best result is achieved by BR (R = 0.91 and MSE = 43.755), while the accuracy of LM is nearly the same (R = 0.90 and MSE = 48.14). LM processes the network in a much shorter time than BR. As a result, LM and BR are the best models in forecasting the 28 days f’c of SCC having RA. The sensitivity analysis showed that cement (28.39%) and water (23.47%) are the most critical variables for predicting the 28-day compressive strength of SCC with RA, while coarse aggregate contributes the least (9.23%). A considerable amount of discarded building materials are produced each year worldwide, resulting in ecosystem degradation. Self-compacting concrete (SCC) has 60-70% coarse and fine particles in its composition, so replacing this material with another waste material, such as recycled aggregate (RA), reduces the cost of SCC. This study compares novel Artificial Neural Network algorithm techniques-Levenberg-Marquardt (LM), Bayesian regularization (BR), and Scaled Conjugate Gradient Backpropagation (SCGB)-to estimate the 28-day compressive strength (f'c) of SCC with RA. A total of 515 samples were collected from various published papers, randomly splitting into training, validation, and testing with percentages of 70, 10 and 20. Two statistical indicators, correlation coefficient (R) and mean squared error (MSE), were used to assess the models; the greater the R and lower the MSE, the more accurate the algorithm. The findings demonstrate the higher accuracy of the three models. The best result is achieved by BR (R = 0.91 and MSE = 43.755), while the accuracy of LM is nearly the same (R = 0.90 and MSE = 48.14). LM processes the network in a much shorter time than BR. As a result, LM and BR are the best models in forecasting the 28 days f'c of SCC having RA. The sensitivity analysis showed that cement (28.39%) and water (23.47%) are the most critical variables for predicting the 28-day compressive strength of SCC with RA, while coarse aggregate contributes the least (9.23%).A considerable amount of discarded building materials are produced each year worldwide, resulting in ecosystem degradation. Self-compacting concrete (SCC) has 60-70% coarse and fine particles in its composition, so replacing this material with another waste material, such as recycled aggregate (RA), reduces the cost of SCC. This study compares novel Artificial Neural Network algorithm techniques-Levenberg-Marquardt (LM), Bayesian regularization (BR), and Scaled Conjugate Gradient Backpropagation (SCGB)-to estimate the 28-day compressive strength (f'c) of SCC with RA. A total of 515 samples were collected from various published papers, randomly splitting into training, validation, and testing with percentages of 70, 10 and 20. Two statistical indicators, correlation coefficient (R) and mean squared error (MSE), were used to assess the models; the greater the R and lower the MSE, the more accurate the algorithm. The findings demonstrate the higher accuracy of the three models. The best result is achieved by BR (R = 0.91 and MSE = 43.755), while the accuracy of LM is nearly the same (R = 0.90 and MSE = 48.14). LM processes the network in a much shorter time than BR. As a result, LM and BR are the best models in forecasting the 28 days f'c of SCC having RA. The sensitivity analysis showed that cement (28.39%) and water (23.47%) are the most critical variables for predicting the 28-day compressive strength of SCC with RA, while coarse aggregate contributes the least (9.23%).
Author	de-Prado-Gil, Jesús Palencia, Covadonga Jagadesh, P. Martínez-García, Rebeca
AuthorAffiliation	3 Department of Mining Technology, Topography, and Structures, Campus de Vegazana s/n, University of León, 24071 León, Spain; rmartg@unileon.es 2 Department of Civil Engineering, Coimbatore Institute of Technology, Coimbatore 638056, Tamil Nadu, India; jaga.86@gmail.com 1 Department of Applied Physics, Campus de Vegazana s/n, University of León, 24071 León, Spain; c.palencia@unileon.es
AuthorAffiliation_xml	– name: 3 Department of Mining Technology, Topography, and Structures, Campus de Vegazana s/n, University of León, 24071 León, Spain; rmartg@unileon.es – name: 1 Department of Applied Physics, Campus de Vegazana s/n, University of León, 24071 León, Spain; c.palencia@unileon.es – name: 2 Department of Civil Engineering, Coimbatore Institute of Technology, Coimbatore 638056, Tamil Nadu, India; jaga.86@gmail.com
Author_xml	– sequence: 1 givenname: Jesús surname: de-Prado-Gil fullname: de-Prado-Gil, Jesús – sequence: 2 givenname: Covadonga orcidid: 0000-0002-4895-2946 surname: Palencia fullname: Palencia, Covadonga – sequence: 3 givenname: P. orcidid: 0000-0002-9803-748X surname: Jagadesh fullname: Jagadesh, P. – sequence: 4 givenname: Rebeca orcidid: 0000-0002-8974-5759 surname: Martínez-García fullname: Martínez-García, Rebeca
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Keywords	compressive strength artificial neural network self-compacting concrete recycled aggregates Bayesian regularization Levenberg–Marquardt Scaled Conjugate Gradient Backpropagation
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Snippet	A considerable amount of discarded building materials are produced each year worldwide, resulting in ecosystem degradation. Self-compacting concrete (SCC) has...
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SubjectTerms	Accuracy Aggregates Algorithms Artificial intelligence Artificial neural networks Back propagation Back propagation networks Brain Building materials Compressive strength Concrete aggregates Construction materials Correlation coefficients Deep learning Machine learning Mathematical functions Neural networks Recycled materials Regularization Self-compacting concrete Sensitivity analysis Variables
Title	A Study on the Prediction of Compressive Strength of Self-Compacting Recycled Aggregate Concrete Utilizing Novel Computational Approaches
URI	https://www.ncbi.nlm.nih.gov/pubmed/35955167 https://www.proquest.com/docview/2700740209 https://www.proquest.com/docview/2702188206 https://pubmed.ncbi.nlm.nih.gov/PMC9370039
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