Prediction of Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete Using Novel Deep Learning Methods
The composition of self-compacting concrete (SCC) contains 60–70% coarse and fine aggregates, which are replaced by construction waste, such as recycled aggregates (RA). However, the complexity of its structure requires a time-consuming mixed design. Currently, many researchers are studying the pred...
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| Format: | Article |
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MDPI AG
2022-06-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/10/13/2245 |
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| author | Jesús de-Prado-Gil Osama Zaid Covadonga Palencia Rebeca Martínez-García |
| author_facet | Jesús de-Prado-Gil Osama Zaid Covadonga Palencia Rebeca Martínez-García |
| author_sort | Jesús de-Prado-Gil |
| collection | DOAJ |
| description | The composition of self-compacting concrete (SCC) contains 60–70% coarse and fine aggregates, which are replaced by construction waste, such as recycled aggregates (RA). However, the complexity of its structure requires a time-consuming mixed design. Currently, many researchers are studying the prediction of concrete properties using soft computing techniques, which will eventually reduce environmental degradation and other material waste. There have been very limited and contradicting studies regarding prediction using different ANN algorithms. This paper aimed to predict the 28-day splitting tensile strength of SCC with RA using the artificial neural network technique by comparing the following algorithms: Levenberg–Marquardt (LM), Bayesian regularization (BR), and Scaled Conjugate Gradient Backpropagation (SCGB). There have been very limited and contradicting studies regarding prediction by using and comparing different ANN algorithms, so a total of 381 samples were collected from various published journals. The input variables were cement, admixture, water, fine and coarse aggregates, and superplasticizer; the data were randomly divided into three sets—training (60%), validation (10%), and testing (30%)—with 10 neurons in the hidden layer. The models were evaluated by the mean squared error (MSE) and correlation coefficient (R). The results indicated that all three models have optimal accuracy; still, BR gave the best performance (R = 0.91 and MSE = 0.2087) compared with LM and SCG. BR was the best model for predicting TS at 28 days for SCC with RA. The sensitivity analysis indicated that cement (30.07%) was the variable that contributed the most to the prediction of TS at 28 days for SCC with RA, and water (2.39%) contributed the least. |
| first_indexed | 2024-03-09T10:27:30Z |
| format | Article |
| id | doaj.art-baed49b2b4f64c9eb939beb990069a1a |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T10:27:30Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-baed49b2b4f64c9eb939beb990069a1a2023-12-01T21:35:16ZengMDPI AGMathematics2227-73902022-06-011013224510.3390/math10132245Prediction of Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete Using Novel Deep Learning MethodsJesús de-Prado-Gil0Osama Zaid1Covadonga Palencia2Rebeca Martínez-García3Department of Applied Physics, Campus of Vegazana s/n, University of León, 24071 León, SpainDepartment of Structure Engineering, Military College of Engineering, Risalpur, National University of Sciences and Technology, Islamabad 44000, PakistanDepartment of Applied Physics, Campus of Vegazana s/n, University of León, 24071 León, SpainDepartment of Mining Technology, Topography and Structures, Campus de Vegazana s/n, University of León, 24071 León, SpainThe composition of self-compacting concrete (SCC) contains 60–70% coarse and fine aggregates, which are replaced by construction waste, such as recycled aggregates (RA). However, the complexity of its structure requires a time-consuming mixed design. Currently, many researchers are studying the prediction of concrete properties using soft computing techniques, which will eventually reduce environmental degradation and other material waste. There have been very limited and contradicting studies regarding prediction using different ANN algorithms. This paper aimed to predict the 28-day splitting tensile strength of SCC with RA using the artificial neural network technique by comparing the following algorithms: Levenberg–Marquardt (LM), Bayesian regularization (BR), and Scaled Conjugate Gradient Backpropagation (SCGB). There have been very limited and contradicting studies regarding prediction by using and comparing different ANN algorithms, so a total of 381 samples were collected from various published journals. The input variables were cement, admixture, water, fine and coarse aggregates, and superplasticizer; the data were randomly divided into three sets—training (60%), validation (10%), and testing (30%)—with 10 neurons in the hidden layer. The models were evaluated by the mean squared error (MSE) and correlation coefficient (R). The results indicated that all three models have optimal accuracy; still, BR gave the best performance (R = 0.91 and MSE = 0.2087) compared with LM and SCG. BR was the best model for predicting TS at 28 days for SCC with RA. The sensitivity analysis indicated that cement (30.07%) was the variable that contributed the most to the prediction of TS at 28 days for SCC with RA, and water (2.39%) contributed the least.https://www.mdpi.com/2227-7390/10/13/2245artificial neural networkself-compacting concreterecycled aggregatestensile strengthLevenberg–MarquardtBayesian regularization |
| spellingShingle | Jesús de-Prado-Gil Osama Zaid Covadonga Palencia Rebeca Martínez-García Prediction of Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete Using Novel Deep Learning Methods Mathematics artificial neural network self-compacting concrete recycled aggregates tensile strength Levenberg–Marquardt Bayesian regularization |
| title | Prediction of Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete Using Novel Deep Learning Methods |
| title_full | Prediction of Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete Using Novel Deep Learning Methods |
| title_fullStr | Prediction of Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete Using Novel Deep Learning Methods |
| title_full_unstemmed | Prediction of Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete Using Novel Deep Learning Methods |
| title_short | Prediction of Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete Using Novel Deep Learning Methods |
| title_sort | prediction of splitting tensile strength of self compacting recycled aggregate concrete using novel deep learning methods |
| topic | artificial neural network self-compacting concrete recycled aggregates tensile strength Levenberg–Marquardt Bayesian regularization |
| url | https://www.mdpi.com/2227-7390/10/13/2245 |
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