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

Ehsan Momeni, Fereydoon Omidinasab (), Ahmad Dalvand, Vahid Goodarzimehr and Abas Eskandari
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Ehsan Momeni: Faculty of Engineering, Lorestan University, Khorramabad 68151-44316, Iran
Fereydoon Omidinasab: Faculty of Engineering, Lorestan University, Khorramabad 68151-44316, Iran
Ahmad Dalvand: Faculty of Engineering, Lorestan University, Khorramabad 68151-44316, Iran
Vahid Goodarzimehr: Department of Civil Engineering, Shahid Bahonar University of Kerman, Kerman 76169-13439, Iran
Abas Eskandari: Faculty of Engineering, Lorestan University, Khorramabad 68151-44316, Iran

Sustainability, 2022, vol. 14, issue 18, 1-20

Abstract: 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.

Keywords: ANN; experimental test; flexural strength; ICA; PSO; recycled aggregate; soft computing (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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