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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Vydáno v:	Materials Ročník 15; číslo 15; s. 5232
Hlavní autoři:	de-Prado-Gil, Jesús, Palencia, Covadonga, Jagadesh, P., Martínez-García, Rebeca
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Switzerland MDPI AG 28.07.2022 MDPI
Témata:	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
On-line přístup:	Získat plný text
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Shrnutí:	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%).
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1996-1944 1996-1944
DOI:	10.3390/ma15155232