Forecasting Compressive Strength of Concrete Containing Nano-Silica Using Particle Swarm Optimization Algorithm and Genetic Algorithm

Abstract

The compressive strength of cement relies upon the properties and its constituents utilized in the right proportions optimization algorithms is a software engineering field that plans and studies effective computational techniques for tackling any problem. The purpose of this study is to forecast the compressive strength (CS) of concrete containing nano-silica using genetic algorithm (GA) and particle swarm optimization (PSO) techniques. In the present investigation, a connection between several input parameters and an output parameter was developed, and it is presented as an improved approach. Even though both GA and PSO are a significant portion of evolutionary optimization algorithms, it was seen through the results obtained that PSO performed better in terms of strength development for concrete in 28 days than GA. The optimum value of CS of concrete is found to be 84.1225 MPa on applying GA technique while the best fit value from the PSO technique came out to be 127.1286 MPa. The Genetic Algorithm is inefficient at dealing with complexity, but the PSO is continuous and discovers the global optima. It is the best alternative, needing fewer parameters and repetitions.