Comparative Study in Design of Fiber-Reinforced Concrete at Elevated Temperatures by Numerical Evaluation through Developed Hybrid Metaheuristic Algorithms

Fibrous concrete has good properties such as high ductility, strength, suitable energy absorption and cracking resistance, which can be useful in many applications. This type of is one the best materials used construction impact-resistant masonries, burial masonry structures, explosive warehouses. In this study, an artificial intelligence assessment based on experimental test data from a laboratory been performed fibrous to evaluate behavior samples at elevated temperatures determine most governing parameter mechanical temperatures. For first time, hybrid algorithm developed neural network structure using both genetic swarm optimization algorithms. The ANFIS-PSO-GA (APG) was trained with evaluated flexural load deflection samples. order detect prominent feature fire resistance concrete, five different subdatasets were designed. results APG have challenged ANFIS-PSO algorithm, well-known numerical evaluation algorithm. As per results, newly designed successfully prediction phases. Based achievements, fiber features content are factors