Flexural Strength of Concrete Beams Made of Recycled Aggregates: An Experimental and Soft Computing-Based Study

The implementation of recycled concrete aggregates (RCAs) in the construction industry has been highlighted in the literature recently. This study aimed to propose an intelligent model for predicting the ultimate flexural strength of recycled reinforced concrete (RRC) beams. For this reason, a datab...

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Bibliographic Details
Published in:Sustainability 2022-09, Vol.14 (18), p.11769
Main Authors: Momeni, Ehsan, Omidinasab, Fereydoon, Dalvand, Ahmad, Goodarzimehr, Vahid, Eskandari, Abas
Format: Article
Language:English
Subjects:
Aggregates
Aggregates (Building materials)
Artificial intelligence
Compressive strength
Concrete
Concrete aggregates
Construction industry
Correlation coefficient
Correlation coefficients
Evolutionary algorithms
Flexural strength
Masonry
Mechanical properties
Neural networks
Optimization
Prediction models
Reinforced concrete
Shear strength
Soft computing
Tensile strength
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Summary:The implementation of recycled concrete aggregates (RCAs) in the construction industry has been highlighted in the literature recently. This study aimed to propose an intelligent model for predicting the ultimate flexural strength of recycled reinforced concrete (RRC) beams. For this reason, a database comprising experimental tests on concrete beams was compiled from the literature. Additionally, two experimental tests were performed in the laboratory to enhance the aforementioned database. The flexural test results showed a 10% reduction in flexural strength when the RRC beam was tested instead of a conventional beam (constructed with natural aggregates). Nevertheless, an artificial neural network (ANN) improved by particle swarm optimization (PSO), as well as an imperialist competitive algorithm (ICA), were utilized for developing the predictive model. The inputs of the hybrid predictive models of flexural strength were the beam geometrical properties, reinforcement ratio, RCA percentage, compressive strength of concrete, and the yield strength of steel. The overall findings (e.g., correlation coefficient values of 0.997 and 0.994 for the testing data) showed the feasibility of the PSO-based ANN predictive model, as well as the ICA-based ANN predictive model in the flexural assessment of RRC beams. Furthermore, comparing the prediction performances of PSO-based ANN with ICA-based ANN and the conventional ANN showed that the PSO-based ANN model outperformed the predictive model built with the conventional ANN and the ICA-ANN.
ISSN:2071-1050
2071-1050
DOI:10.3390/su141811769