A Novel Method for Optimizing Parameters influencing the Bearing Capacity of Geosynthetic Reinforced Sand Using RSM, ANN, and Multi-objective Genetic Algorithm

Abstract In this study, a novel method is proposed to optimize the reinforced parameters influencing bearing capacity of shallow square foundation resting on sandy soil with geosynthetic. The be optimized are reinforcement length (L), number layers ( N ), depth topmost layer geosynthetic (U), and vertical distance between two (X). To achieve objective, 25 laboratory small-scale model tests were conducted sand. This laboratory-scale has used geosynthetics as materials one soil. Firstly, effect load was investigated using analysis variance (ANOVA). Both response surface methodology (RSM) artificial neural networks (ANN) tools applied compared capacity. Finally, multiobjective genetic algorithm (MOGA) coupled RSM ANN models solve multi objective optimization problems. design considered multi-objective problem. regard, conflicting objectives need maximize minimize cost. According obtained results, an informed decision regarding sand reached.