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The performance comparison of the soft computing methods on the prediction of soil compaction parameters

机译:软计算方法对土壤压实参数预测的性能比较

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摘要

The compaction parameters of soils known as the optimum moisture content (OMC) and maximum dry density (MDD) are necessary for the geotechnical engineering applications such as the fills, embankments, and dams. However, it takes a long time to determine the compaction parameters due to the laboratory test procedure. It was aimed to estimate the compaction parameters of soils with four soft computing methods and also to compare the performance of the methods in this study. For this purpose, a wide database consisting the index and standard proctor (SP) test results were used. Although all AI methods used in this study are successful on estimation of the MDD and OMC parameters, it was seen that the ELM method was the most successful method on the prediction of compaction parameters.
机译:被称为最佳水分含量(OMC)和最大干密度(MDD)的土壤的压实参数是岩土工程应用,例如填充,堤防和水坝等岩土工程应用。 然而,由于实验室测试程序,确定压缩参数需要很长时间。 旨在估算具有四种软计算方法的土壤的压实参数,也可以比较本研究中方法的性能。 为此目的,使用宽的数据库,包括索引和标准Proctor(SP)测试结果。 尽管本研究中使用的所有AI方法都是在估计MDD和OMC参数的估计上的成功,但是可以看出ELM方法是对压实参数预测的最成功的方法。

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