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首页> 外文期刊>European Physical Journal Plus >Prediction of the California bearing ratio (CBR) of compacted soils by using GMDH-type neural network
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Prediction of the California bearing ratio (CBR) of compacted soils by using GMDH-type neural network

机译：用GMDH型神经网络预测压实土的加州轴承比（CBR）

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摘要

The California bearing ratio (CBR) is an important parameter in defining the bearing capacity of various soil structures, such as earth dams, road fillings and airport pavements. However, determination of the CBR value of compacted soils from tests takes a relatively long time and leads to a demanding experimental working program in the laboratory. This study is aimed to predict the CBR value of compacted soils by using the group method of data handling (GMDH) model with a type of artificial neural networks (ANN). The results were also compared with multiple linear regression (MLR) analysis and different ANN models. The selected variables for the developed models are gravel content (GC), sand content (SC) fine content (FC), liquid limit (LL), plasticity index (PI), optimum moisture content (OMC) and maximum dry density (MDD) of compacted soils. Many trials were carried out with different numbers of layers and different numbers of neurons in the hidden layer in GMDH model and with different training algorithms in ANN models. The results indicate that the GMDH model has better success in the estimation of the CBR value compared to both the MLR and the different types of ANN models.
机译：加州轴承比（CBR）是定义各种土壤结构的承载力的重要参数，如地球坝，道路填充和机场路面。然而，测定从测试中的压实土壤的CBR值需要相对较长的时间，并导致实验室中的苛刻的实验工作程序。本研究旨在通过使用具有一种人工神经网络（ANN）的数据处理（GMDH）模型的组方法来预测压实土壤的CBR值。结果也与多元线性回归（MLR）分析和不同的ANN模型进行了比较。开发模型的所选变量是砾石含量（GC），砂含量（SC）细含量（Fc），液体限制（LL），塑性指数（PI），最佳水分含量（OMC）和最大干密度（MDD）压实的土壤。许多试验以不同数量的层和不同数量的神经元在GMDH模型中的隐藏层中进行，并且在ANN模型中具有不同的训练算法。结果表明，与MLR和不同类型的ANN模型相比，GMDH模型在CBR值估计中具有更好的成功。

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