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 behavior 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), $e_0$ (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, 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.

这项研究的重点是三种软计算技术的应用，包括最小极大概率机器回归（MPMR），基于粒子群优化的人工神经网络（ANN-PSO）和基于粒子群优化的自适应网络模糊推理系统（ANFIS-PSO）。基于沉降准则的浅层地基可靠性。土壤是一种非均质的介质，其基础力学特性在土壤基础系统中的参与使得对浅层沉降的预测成为一个复杂的工程问题。这项研究探讨了针对确定性方法的软计算技术的可行性。浅层地基的沉降取决于参数γ（单位重量），${Ë}_0$ （无效比）和 $C_C$（压缩指数）。这些土壤参数作为输入变量，浅层基础沉降作为输出变量。为了评估模型的性能，使用了不同的性能指标，即RMSE，VAF，R ²，偏差因子，MAPE，LMI，U ₉₅，RSR，NS，RPD等。从结果分析发现，MPMR模型优于PSO-ANFIS和PSO-ANN。因此，MPMR可以用作解决浅层基础在土壤上的非线性问题的可靠软计算技术。