Predictive analysis of concrete tensile strength using metaheuristic algorithms helping a neural network

Abstract In most construction projects, concrete is a widely used material in different structural elements due to its suitable mechanical behavior. Therefore, it is crucial to achieve a dependable estimation of the concrete mechanical parameters such as tensile strength. In this research, a well-known machine learning model—multi-layer perceptron neural network (MLPNN)—is optimized by multi-tracker optimization algorithm (MTOA) to avoid computational insufficiencies. The model predicts the splitting tensile strength of concrete based on the features of the concrete mixture. For validation,