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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Vydané v:	Materials Ročník 15; číslo 15; s. 5232
Hlavní autori:	de-Prado-Gil, Jesús, Palencia, Covadonga, Jagadesh, P., Martínez-García, Rebeca
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Switzerland MDPI AG 28.07.2022 MDPI
Predmet:	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 prístup:	Získať plný text
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