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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Veröffentlicht in:	Materials Jg. 15; H. 15; S. 5232
Hauptverfasser:	de-Prado-Gil, Jesús, Palencia, Covadonga, Jagadesh, P., Martínez-García, Rebeca
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland MDPI AG 28.07.2022 MDPI
Schlagworte:	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
Online-Zugang:	Volltext
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