Optimization-Based Fuzzy System Application on Deformation of Geogrid-Reinforced Soil Structures

Abstract When it comes to the geosynthetic-reinforced soil structures’ structural design, the analysis of deformation is of the highest relevance. In spite of this, the academic literature lays a substantial amount of attention on the potential of artificial intelligence approaches in efficiently addressing the many challenges that are faced in geotechnical engineering. The investigation of the possible use of approaches based on machine learning for the purpose of forecasting the geogrid-reinforced soil structures’ deformation ( $${\text{Dis}}$$ Dis ) was the major focus of this study. These efforts are made to reduce the time and cost of numerical modeling. This study aimed to enable computers to learn patterns and insights from data to make accurate predictions or decisions about unseen data. This paper introduces novel systems that coupled the Beluga whale optimizer ( $${\text{BWH}}$$ BWH ), Henry gas solubility optimization ( $${\text{HGSO}}$$ HGSO ), gannet optimization algorithm ( $${\text{GOA}}$$ GOA ), and Harris hawks optimizer ( $${\text{HHO}}$$ HHO ) with adaptive neuro-fuzzy inference system ( $${\text{ANFIS}}$$ ANFIS ). A dataset was created by gathering 166 finite element analyses accomplished in the literature. Between four $${\text{ANFIS}}$$ ANFIS systems, the integrated one with $${\text{GOA}}$$ GOA got the largest accuracy value, accounting for 0.9841 and 0.9895 in the train and test stages, better than $${\text{ANF}}_{{{\text{BWH}}}}$$ ANF BWH , followed by $${\text{ANF}}_{{{\text{HH}}}}$$ ANF HH . It was seen from $${\text{U}}_{{{95}}} { }$$ U 95 that the $${\text{ANF}}_{{{\text{GOA}}}} { }$$ ANF GOA scenario exhibits the least level of uncertainty in comparison to other models, hence demonstrating its greater capacity for generalization. Between four $${\text{ANFIS}}$$ ANFIS systems, the most accurate system with the lowest $${\text{OBJ}}$$ OBJ value is $${\text{ANF}}_{{{\text{GOA}}}}$$ ANF GOA at 2.6098, followed by $${\text{ANF}}_{{{\text{BWH}}}}$$ ANF BWH at 2.8002. Sensitivity analysis depicts that removing the surcharge $$\left( q \right)$$ q parameter from the input group has a considerable negative impact on output accuracy.