Load-Settlement Curve and Subgrade Reaction of Strip Footing on Bi-Layered Soil Using Constitutive FEM-AI Coupled Techniques

Abstract

This study presents a hybrid Artificial Intelligence-Finite Element Method (AI-FEM) predictive model to estimate the modulus of a subgrade reaction of a strip footing rested on a bi-layered profile. A parametric study was carried out using 2D Plaxis FEM models for strip footings with width (B) and rested on a bi-layered profile with top layer thickness (h) and bottom layer thickness (H). The soil was modeled using the well-known Mohr-Coulomb’s constitutive law. The extracted load-settlement curve from each FEM model is approximated to hyperbolic function and its factors (a, b) were determined. The subgrade reaction value (Ks) is the (stress/settlement), hence (1/Ks = a·Δ + b). Both inputs and outputs of the parametric study were collected in a single database containing the geometrical factors (B, h & H), soil properties of the top and bottom layers (c, φ & γ) and the extracted hyperbolic factors (a, b). Finally, three AI techniques—Genetic Programming (GP), Evolutionary Polynomial Regression (EPR) and Artificial Neural Networks (ANN)—were implemented to develop three predictive models to estimate the values of (a, b) using the collected database. The three developed models showed different accuracy values of (50%, 65% and 80%) for (GP, EPR and ANN), respectively. The innovation of the developed model is its ability to capture the degradation of a subgrade reaction by increasing the stress (or the settlement) according to the hyperbolic formula.