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Fuzzy-Based Model for Predicting Strength of Geogrid-Reinforced Subgrade Soil with Optimal Depth of Geogrid Reinforcement

机译：基于基于Geogrid加固的地质钢化钢化土壤预测力的模糊模型

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摘要

A fuzzy logic (FL)-based modeling approach is employed for geogrid-reinforced subgrade soil of unpaved roads. A review of the literature reveals that fuzzy logic has not been used for predicting the behavior of geogrid-reinforced subgrade. This paper presents FL-based two models with fuzzy Triangular and Gaussian membership functions for input and output variables. It consists of eight input parameters/factors, namely, reinforced/unreinforced section, depth of reinforcement, liquid limit, plastic limit, plasticity index, optimum moisture content, maximum dry unit weight, and soaked/unsoaked condition, and California bearing ratio (CBR) as an output parameter. The fuzzy rules are deduced from the experimental data. The laboratory CBR tests were performed on the subgrade soil reinforced with geogrid. The precision of models was examined by comparing the predicted CBR values with the experimental CBR values for Triangular and Gaussian membership functions. The sensitivity analysis reflects a set of dominant parameters. The results indicated a significant improvement in the CBR value of geogrid-reinforced subgrade soil due to the inclusion of geogrid. The range for optimal depth of geogrid reinforcement is found to be 36 to 60% of the thickness of the soil layer. The potentialities of FL were found to be satisfactory.
机译：基于覆盖道路的地质钢化钢化土壤采用模糊逻辑（FL）的建模方法。对文献的审查揭示了模糊逻辑未被用来预测地质格栅加强路基的行为。本文介绍了一种基于FL的两种模型，具有用于输入和输出变量的模糊三角形和高斯成员函数。它由八个输入参数/因素组成，即加固/未合成的部分，加固深度，液体限制，塑料极限，可塑性指数，最佳水分含量，最大干燥单位重量和浸泡/未烘烤的条件，以及加州轴承比（CBR ）作为输出参数。从实验数据推导出模糊规则。在用地质格栅加固的Dropgrade土壤上进行实验室CBR试验。通过将预测的CBR值与三角形和高斯成员函数的实验CBR值进行比较来检查模型的精度。敏感性分析反映了一组显性参数。结果表明由于含有土工格栅而导致土工格栅增强路基土壤的CBR值的显着改善。土工格栅加固的最佳深度范围被发现为土壤层厚度的36至60％。发现FL的潜力是令人满意的。

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