Application of soft computing techniques for shallow foundation reliability in geotechnical engineering

This research focuses on the application of three soft computing techniques including Minimax Probability Machine Regression (MPMR), Particle Swarm Optimization based Artificial Neural Network (ANN-PSO) and Particle Swarm Optimization based Adaptive Network Fuzzy Inference System (ANFIS-PSO) to study the shallow foundation reliability based on settlement criteria. Soil is a heterogeneous medium and the involvement of its attributes for geotechnical behaviour in soil-foundation system makes the prediction of settlement of shallow a complex engineering problem. This study explores the feasibility of soft computing techniques against the deterministic approach. The settlement of shallow foundation depends on the parameters gamma (unit weight), e(0) (void ratio) and C-C (compression index). These soil parameters are taken as input variables while the settlement of shallow foundation as output. To assess the performance of models, different performance indices i.e. RMSE, VAF, R-2, Bias Factor, MAPE, LMI, U-95, RSR, NS, RPD, etc. were used. From the analysis of results, it was found that MPMR model outperformed PSO-ANFIS and PSO-ANN. Therefore, MPMR can be used as a reliable soft computing technique for non-linear problems for settlement of shallow foundations on soils.

Automated summary

This research focuses on using three soft computing techniques to analyze the shallow foundation settlement based on reliability criteria. The study found that Minimax Probability Machine Regression model outperformed Particle Swarm Optimization based Artificial Neural Network and Particle Swarm Optimization based Adaptive Network Fuzzy Inference System, making it a reliable soft computing technique for nonlinear settlement problems in soil foundations.