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<p>In this study, it is aimed to provide significant advantages in terms of time and cost by estimating critical standard compaction parameters such as maximum dry density (MDD) and optimum moisture content (OMC) with machine learning methods instead of traditional laboratory tests. A large dataset including different soil components such as gravel, sand, fine-grained, liquid limit (LL), plastic limit (PL) and plasticity index (PI) was used and algorithms such as decision tree, random forest, gradient boosting and group data processing method (GMDH) were compared. Model performances were evaluated using metrics such as R<InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12665_2025_12328_Article_IEq1.gif" Format="GIF" Height="10" Rendition="HTML" Resolution="72" Type="Linedraw" Width="8" /> </InlineMediaObject> <EquationSource Format="TEX">\(^{2}\)</EquationSource> <EquationSource Format="MATHML"><math> <mmultiscripts> <mrow /> <mrow /> <mn>2</mn> </mmultiscripts> </math></EquationSource> </InlineEquation> (coefficient of determination) and RMSE (root mean square error). The results show that the gradient boosting algorithm achieved high accuracy in estimating optimum moisture content (OMC) with a testing R<InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12665_2025_12328_Article_IEq1.gif" Format="GIF" Height="10" Rendition="HTML" Resolution="72" Type="Linedraw" Width="8" /> </InlineMediaObject> <EquationSource Format="TEX">\(^{2}\)</EquationSource> <EquationSource Format="MATHML"><math> <mmultiscripts> <mrow /> <mrow /> <mn>2</mn> </mmultiscripts> </math></EquationSource> </InlineEquation> value of 0.91, while the random forest algorithm was the most successful model in estimating maximum dry density (MDD) with a testing R<InlineEquation ID="IEq3"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12665_2025_12328_Article_IEq1.gif" Format="GIF" Height="10" Rendition="HTML" Resolution="72" Type="Linedraw" Width="8" /> </InlineMediaObject> <EquationSource Format="TEX">\(^{2}\)</EquationSource> <EquationSource Format="MATHML"><math> <mmultiscripts> <mrow /> <mrow /> <mn>2</mn> </mmultiscripts> </math></EquationSource> </InlineEquation> value of 0.92. Machine learning models have been shown to provide faster and lower-cost predictions by reducing the dependency on laboratory tests and offer an effective alternative for soil standard compaction analyses in geotechnical engineering.</p>

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Machine learning-based prediction of soil compaction parameters

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月9日

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  • Muharrem Atakan Senturk,
  • Ertugrul Ordu,
  • Rabia Korkmaz Tan

摘要

In this study, it is aimed to provide significant advantages in terms of time and cost by estimating critical standard compaction parameters such as maximum dry density (MDD) and optimum moisture content (OMC) with machine learning methods instead of traditional laboratory tests. A large dataset including different soil components such as gravel, sand, fine-grained, liquid limit (LL), plastic limit (PL) and plasticity index (PI) was used and algorithms such as decision tree, random forest, gradient boosting and group data processing method (GMDH) were compared. Model performances were evaluated using metrics such as R \(^{2}\) 2 (coefficient of determination) and RMSE (root mean square error). The results show that the gradient boosting algorithm achieved high accuracy in estimating optimum moisture content (OMC) with a testing R \(^{2}\) 2 value of 0.91, while the random forest algorithm was the most successful model in estimating maximum dry density (MDD) with a testing R \(^{2}\) 2 value of 0.92. Machine learning models have been shown to provide faster and lower-cost predictions by reducing the dependency on laboratory tests and offer an effective alternative for soil standard compaction analyses in geotechnical engineering.