We examine the outcome of popular artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) for estimating the shear strength parameters of c − φ soil. A matrix of one hundred twelve datasets collected using in situ and laboratory tests to train and test the ANN and ANFIS models. Standard penetration test number of blows value along with the soil properties taken as input vectors, whereas shear strength parameters like cohesion (c) and angle of internal friction (ϕ) used as target vectors. The minimum validation error has been employed as the stopping criterion to avoid over fitting in the analysis. Out of four developed models, predicted values through two ANN models were close to actual value in comparison to ANFIS models. Statistical parameters such as coefficient of correlation, root mean square error and average absolute error were used as performance evaluation measures. Based on statistical measures it was observed that performances of ANN and ANFIS models were in accordance with the experimental results and it could substitute tedious laboratory work provided sufficient and reliable data source are offered. The results through performance evaluation measures also reveal that ANN and ANFIS models are effective, versatile and useful way to measure the shear strength parameters of soils.

我们研究了流行的人工神经网络（ANN）和自适应神经模糊推理系统（ANFIS）的结果，用于估计c  -  φ土的抗剪强度参数。使用现场测试和实验室测试收集的112个数据集的矩阵，以训练和测试ANN和ANFIS模型。标准击穿试验的击打次数值与土壤特性一起作为输入矢量，而抗剪强度参数（如内聚力（c）和内摩擦角（ϕ））用作目标向量。最小验证误差已被用作终止标准，以避免过度拟合分析。在四个已开发模型中，与ANFIS模型相比，通过两个ANN模型的预测值接近实际值。相关系数，均方根误差和平均绝对误差等统计参数被用作性能评估手段。根据统计方法，观察到ANN和ANFIS模型的性能与实验结果一致，只要提供了足够可靠的数据源，它就可以代替繁琐的实验室工作。通过性能评估的结果还表明，ANN和ANFIS模型是测量土壤抗剪强度参数的有效，通用和有用的方法。