investigation of evolutionary optimization algorithms for estimating sandstone compressive strength

Directly determining rock compressive strength is both laborious and expensive. as a result, indirect estimation methods, such as artificial neural networks, are employed. input parameters, such as quartz content, dry density, and brazilian tensile strength, have been used to predict the compressive strength of sandstone. genetic algorithm (ga) and particle swarm optimization (pso) were selected to improve network training using evolutionary optimization algorithms effectiveness. the results demonstrate that the pso model achieved the best estimation performance with an of 0.0214 and of 0.95. the linear regression model exhibited inferior performance with an of 0.87.