Predicting Confinement Effect of Carbon Fiber Reinforced Polymers on Strength of Concrete using Metaheuristics-based Artificial Neural Networks
This work addresses the problem of time-consuming and costly experimental testing for civil engineers by offering accurate predictive models for CFRP-confined concrete strength, though it is incremental as it applies existing metaheuristic methods to a specific dataset.
The study tackled predicting the confinement effect of carbon fiber reinforced polymers on concrete cylinder strength using metaheuristics-based artificial neural networks, achieving up to 99.13% accuracy with a hybrid PSO model. This provides a reliable alternative to empirical methods, making the process quicker and more economical by avoiding full-scale experimental tests.
This article deals with the study of predicting the confinement effect of carbon fiber reinforced polymers (CFRPs) on concrete cylinder strength using metaheuristics-based artificial neural networks. A detailed database of 708 CFRP confined concrete cylinders is developed from previously published research with information on 8 parameters including geometrical parameters like the diameter (d) and height (h) of a cylinder, unconfined compressive strength of concrete (fco'), thickness (nt), the elastic modulus of CFRP (Ef), unconfined concrete strain confined concrete strain and the ultimate compressive strength of confined concrete fcc'. Three metaheuristic models are implemented including particle swarm optimization (PSO), grey wolf optimizer (GWO), and bat algorithm (BA). These algorithms are trained on the data using an objective function of mean square error and their predicted results are validated against the experimental studies and finite element analysis. The study shows that the hybrid model of PSO predicted the strength of CFRP-confined concrete cylinders with maximum accuracy of 99.13% and GWO predicted the results with an accuracy of 98.17%. The high accuracy of axial compressive strength predictions demonstrated that these prediction models are a reliable solution to the empirical methods. The prediction models are especially suitable for avoiding full-scale time-consuming experimental tests that make the process quick and economical.