An Artificial Intelligence Technique in Industry 4.0 for Predicting the Settlement of Geocell-Reinforced Soil Foundations

Abstract

Civil and geotechnical engineering professionals face the challenge of settlement prediction to ensure the secure and long-lasting construction of a geocell-reinforced soil foundation (GRSF). In this study, a new adaptive method for forecasting geocell settlement has been developed. It is based on the adaptive artificial neural network (ANN) technique and elephant herding optimization (EHO). The goal is to reduce erosion on steep slopes, strengthen soft ground, and increase the carrying capacity of retaining structures, foundations, roadways, and railroads. The confinement effect, which occurs when the geocell disperses the loads across a larger area and enhances the soil's ability to sustain loads, makes the research novel. Numerical results from plate load tests on unreinforced and geocell-reinforced foundation beds have validated the proposed model.