Predicting CFRP-Confinement Effect on Concrete Strength Using Metaheuristics-Based Neural Networks
The study discussed in this article focuses on using metaheuristics-based artificial neural networks to predict the confinement effect of carbon fiber reinforced polymers (CFRPs) on concrete cylinder strength. This research is significant because it provides a reliable and economical solution to predicting the strength of CFRP-confined concrete cylinders, eliminating the need for time-consuming and expensive experimental tests.
Database Development
A detailed database of 708 CFRP confined concrete cylinders is developed from previously published research. This database includes information on eight parameters, including geometrical parameters (diameter and height of a cylinder), unconfined compressive strength of concrete, thickness, elastic modulus of CFRP, unconfined concrete strain, confined concrete strain, and the ultimate compressive strength of confined concrete. This extensive database ensures that the predictions made by the metaheuristic models are based on a wide range of inputs, enhancing their accuracy and reliability.
Metaheuristic Models
Three metaheuristic models are implemented in this study: particle swarm optimization (PSO), grey wolf optimizer (GWO), and bat algorithm (BA). These metaheuristic algorithms are trained on the database using an objective function of mean square error. By utilizing these algorithms, the researchers are able to optimize the neural network models and improve the accuracy of the predictions.
Accuracy and Validation
The predicted results of the metaheuristic models are validated against experimental studies and finite element analysis. The study shows that the hybrid model of PSO predicted the strength of CFRP-confined concrete cylinders with a maximum accuracy of 99.13%. The GWO model also performed well, with a prediction accuracy of 98.17%. These high accuracies demonstrate that the prediction models developed in this study are a reliable alternative to empirical methods.
Practical Applications
The prediction models developed in this study have practical applications in the construction industry. By using these models, engineers and researchers can avoid the need for full-scale experimental tests, which are time-consuming and expensive. Instead, they can quickly and economically predict the strength of CFRP-confined concrete cylinders, allowing them to make informed decisions and optimize designs without the need for extensive testing.
In conclusion, the study discussed in this article provides valuable insights into using metaheuristics-based artificial neural networks to predict the confinement effect of CFRPs on concrete cylinder strength. The use of metaheuristic algorithms improves the accuracy of the predictions, with the hybrid model of PSO achieving a maximum accuracy of 99.13%. These prediction models have practical applications in the construction industry, allowing for quick and economical predictions without the need for extensive experimental tests. This research contributes to the advancement of efficient and cost-effective design processes in the construction field, ultimately leading to improved structural performance and durability.
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