Prediction of Ultimate Bearing Capacity of Cohesionless Soils Using Soft Computing Techniques

S Adarsh,R Merlin,J Tina,G Krishna,R Dhanya

doi:10.5402/2012/628496

Abstract

This study examines the potential of two soft computing techniques, namely, support vector machines (SVMs) and genetic programming (GP), to predict ultimate bearing capacity of cohesionless soils beneath shallow foundations. The width of footing (), depth of footing (), the length-to-width ratio () of footings, density of soil ( or ), angle of internal friction (), and so forth were used as model input parameters to predict ultimate bearing capacity (). The results of present models were compared with those obtained by three theoretical approaches, artificial neural networks (ANNs), and fuzzy inference system (FIS) reported in the literature. The statistical evaluation of results shows that the presently applied paradigms are better than the theoretical approaches and are competing well with the other soft computing techniques. The performance evaluation of GP model results based on multiple error criteria confirms that GP is very efficient in accurate prediction of ultimate bearing capacity cohesionless soils when compared with other models considered in this study.

Highlights

Design of foundations is performed based on two criteria: ultimate bearing capacity and limiting settlement
The efficiency of the developed models is analyzed by different statistical performance evaluation criteria such as correlation coefficient (R), coefficient of efficiency (E), rootmean-square error (RMSE), mean bias error (MBE), and mean absolute relative error (MARE)
It is to be noted that the artificial neural networks (ANNs) and fuzzy inference system (FIS) results presented in Table 4 are deduced based on the relative error (RE) values reported by Padmini et al [5]

Summary

Introduction

Design of foundations is performed based on two criteria: ultimate bearing capacity and limiting settlement. Artificial neural networks (ANNs) may probably be the most popular among these tools, applied for prediction of bearing capacity of cohesionless soils [5], bearing capacity of piles, settlement predictions, liquefaction, and slope stability problems [6]. The evolutionary computational techniques may be a better alternative for solving regression problems as they follow an optimization strategy with progressive improvement towards the global optima. They start with possible trial solutions within a decision space, and the search is guided by genetic operators and the principle of “survival of the fittest” [11]. SVM and GP are used as alternate paradigms to predict bearing capacity of cohesionless soils under shallow foundations

Support Vector Machine

Genetic Programming

Model Development and Results

Results and Discussions

Conclusions

Best Program