Concepedia

TLDR

Shallow foundation settlement prediction is complex due to soil heterogeneity and dependence on unit weight, void ratio, and compression index. The study investigates the feasibility of using soft computing methods—MPMR, ANN‑PSO, and ANFIS‑PSO—to assess shallow foundation reliability versus deterministic approaches. The models use unit weight, void ratio, and compression index as inputs to predict settlement, and their performance was evaluated with metrics such as RMSE, VAF, R², Bias Factor, MAPE, LMI, U95, RSR, NS, and RPD. The MPMR model outperformed PSO‑ANFIS and PSO‑ANN, indicating it is a reliable soft computing approach for nonlinear settlement prediction of shallow foundations.

Abstract

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 γ (unit weight), e0 (void ratio) and CC (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, R2, Bias Factor, MAPE, LMI, U95, 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.

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