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 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.

Automated summary

This study aimed to develop an intelligent model for predicting the flexural strength of recycled reinforced concrete (RRC) beams. The results showed the feasibility of the PSO-ANN and ICA-ANN models in evaluating the flexural performance of RRC beams, with the PSO-ANN model outperforming the other models in terms of prediction accuracy.