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An evolutionary approach to modelling the soil-water characteristic curve in unsaturated soils

机译:非饱和土壤水-水特征曲线建模的进化方法

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摘要

In this paper a new approach is presented based on evolutionary polynomial regression (EPR) for modelling of soil-water characteristic curve in unsaturated soils. EPR is an evolutionary data mining technique that generates a transparent and structured representation of the behaviour of a system directly from data. This method can operate on large quantities of data in order to capture nonlinear and complex relationships between variables of the system. It also has the additional advantage that it allows the user to gain insight into the behaviour of the system. Results from pressure plate tests carried out on clay, silty clay, sandy loam, and loam are used for developing and validating the EPR model. The model inputs are the initial void ratio, initial gravimetric water content, logarithm of suction normalised with respect to atmospheric air pressure, clay content, and silt content. The model output is the gravimetric water content corresponding to the assigned input suction. The EPR model predictions are compared with the experimental results as well as the models proposed by previous researches. The results show that the proposed approach is very effective and robust in modelling the soil-water characteristic curve in unsaturated soils. The merits and advantages of the proposed approach are highlighted.
机译:本文提出了一种基于进化多项式回归(EPR)的非饱和土壤水特征曲线建模方法。 EPR是一种进化数据挖掘技术,可直接从数据生成透明且结构化的系统行为表示。该方法可以对大量数据进行操作,以捕获系统变量之间的非线性和复杂关系。它还具有其他优势,它允许用户深入了解系统的行为。在粘土,粉质粘土,沙质壤土和壤土上​​进行的压板试验结果用于开发和验证EPR模型。模型输入为初始空隙率,初始重量水含量,相对于大气压标准化的吸力对数,粘土含量和淤泥含量。模型输出是与分配的输入吸力对应的重量含水量。将EPR模型的预测结果与实验结果以及先前研究提出的模型进行比较。结果表明,该方法在非饱和土壤水-水特征曲线建模中非常有效且鲁棒。突出了所提出的方法的优点和优点。

著录项

  • 来源
    《Computers & geosciences》 |2012年第2012期|p.25-33|共9页
  • 作者单位

    Computational Ceomechanics Group, College of Engineering Mathematics and Physical Sciences, University of Exeter, UK;

    Department of Civil and Environmental Engineering, Shiraz University of Technology, Iran;

    Computational Ceomechanics Group, College of Engineering Mathematics and Physical Sciences, University of Exeter, UK;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    SWCC; pressure plate test; data mining; evolutionary computing;

    机译:SWCC;压力板测试;数据挖掘;进化计算;

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