Intelligent modelling and design of soft soil improved with floating column-like elements as a road subgrade

Abstract

The use of stone columns (SC) and deep cement mixing (DCM) columns to stabilize road subgrades are economical and reliable ground improvement techniques that have been extensively studied. The current study used an adaptive neural fuzzy inference system (ANFIS) hybridized with a particle swarm optimization (PSO) algorithm to develop models for estimating the ultimate bearing capacity (UBC) of soft soil improved with floating SC or DCM columns. A database of 86 physical modelling tests was used to create and train the ANFIS-PSO models. The input parameters used were the undrained shear strength of soft soil, area improvement ratio, and length-to-diameter ratio of the floating SC and DCM columns. The performance of the proposed ANFIS-PSO model has been validated using the testing data and a decision-making model for the design of the geotechnical properties of the floating SC and DCM columns has been proposed. The results show that the accuracy of the proposed ANFIS-PSO model in terms of the performance indices was satisfactory. The R2, VAF, and MSE values were obtained for the testing sets of the optimized ANFIS-PSO predictive model. This ANFIS-PSO model can be used by geotechnical engineers for the design of floating DCM and SC columns that increase the UBC of a road subgrade.