Prediction of compressive strength of recycled concrete cylinders reinforced with fiber-reinforced polymer using a combined genetic algorithm and neural network approach.

Hossein Khosravi; Marzie Mohammadi Pour; Mohammad Bahram

doi:10.1038/s41598-026-50254-2

May 2, 2026

Prediction of compressive strength of recycled concrete cylinders reinforced with fiber-reinforced polymer using a combined genetic algorithm and neural network approach.

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Key Points

The research aims to develop a predictive model for the compressive strength of recycled concrete using a genetic algorithm and neural networks.
Used a combined genetic algorithm and neural network approach for prediction.
Conducted sensitivity analysis on various parameters affecting compressive strength.
Evaluated elastic modulus, tensile strength, and FRP layer thickness in the context of stress-strain response.
The model achieved a reliability score of 0.91, indicating high predictive accuracy.
Elastic modulus, tensile strength, and FRP layer thickness significantly influenced stress-strain behavior.

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Implication

Study predicts compressive strength in recycled concrete cylinders, suggesting efficient design possibilities.

Abstract

= 0.91). Sensitivity analysis revealed that the elastic modulus, tensile strength, and thickness of the FRP layers have the most significant impact on the stress-strain response. These results confirm the model's reliability and its potential application in designing sustainable and efficient reinforced concrete structures.

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Khosravi et al. (2026) studied this question.

synapsesocial.com/papers/69f9898f15588823dae18653 https://doi.org/10.1038/s41598-026-50254-2

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Prediction of compressive strength of recycled concrete cylinders reinforced with fiber-reinforced polymer using a combined genetic algorithm and neural network approach.

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

Prediction of compressive strength of recycled concrete cylinders reinforced with fiber-reinforced polymer using a combined genetic algorithm and neural network approach.

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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