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首页> 外文期刊>Mathematical Problems in Engineering >Prediction of Frost-Heaving Behavior of Saline Soil in Western Jilin Province, China, by Neural Network Methods
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Prediction of Frost-Heaving Behavior of Saline Soil in Western Jilin Province, China, by Neural Network Methods

机译：吉林省西部盐渍土冻胀行为的神经网络预测

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摘要

In this study, backpropagation neural network (BPNN) and generalized regression neural network (GRNN) approaches are used to predict the frost-heaving ratio (FR) of the saline soil specimen collected from Nong'an, Western Jilin, China. Four variables, namely, water content (WC), compactness, temperature, and content of soluble salts (CSS), are considered in predicting FR. A total of 360 pieces of data, collected from the experimental results, in which 30 pieces of data were selected randomly as the testing set data and the rest of the data were treated as the training set data, are applied to develop the prediction models. The predicted data from the models are compared with the experimental data. Then, the results of the two approaches are compared to obtain a relatively reliable model. Results indicate that the prediction model for the FR of saline soil in Nong'an can be successfully established using the GRNN method. Four new GRNN models are constructed for sensitivity analysis to assess the influence degree of the influencing factors, and the results indicate that water content is the most influential variable in the FR of the saline soil specimen, whereas content of soluble salts is the least influential variable.
机译：在这项研究中，使用反向传播神经网络（BPNN）和广义回归神经网络（GRNN）方法来预测从吉林西部农安采集的盐渍土壤标本的冻胀率（FR）。预测FR时考虑了四个变量，即水含量（WC），压实度，温度和可溶性盐含量（CSS）。从实验结果中收集了总共360条数据，其中随机选择30条数据作为测试集数据，其余数据作为训练集数据，用于开发预测模型。将来自模型的预测数据与实验数据进行比较。然后，将两种方法的结果进行比较以获得相对可靠的模型。结果表明，利用GRNN方法可以成功建立农安县盐渍土FR的预测模型。建立了四个新的GRNN模型进行敏感性分析，以评估影响因素的影响程度，结果表明，含水量是盐渍土样品FR中影响最大的变量，而可溶性盐含量是影响最小的变量。

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  • 来源
    《Mathematical Problems in Engineering》 |2017年第7期|7689415.1-7689415.10|共10页
  • 作者

    Zhang Xudong; Wang Qing; Huo Zhensheng; Yu Tianwen; Wang Gang; Liu Tianbao; Wang Wenhua;

  • 作者单位

    Jilin Univ, Coll Construct Engn, Changchun 130026, Jilin, Peoples R China;

    Jilin Univ, Coll Construct Engn, Changchun 130026, Jilin, Peoples R China;

    Jilin Univ, Coll Construct Engn, Changchun 130026, Jilin, Peoples R China;

    Jilin Univ, Coll Construct Engn, Changchun 130026, Jilin, Peoples R China;

    Jilin Univ, Coll Construct Engn, Changchun 130026, Jilin, Peoples R China;

    Jilin Univ, Coll Construct Engn, Changchun 130026, Jilin, Peoples R China;

    Changchun Inst Technol, Coll Civil Engn, Changchun 130012, Jilin, Peoples R China;

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