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<p>Moisture content in geomaterials critically impacts road construction. Optimising the Optimum Moisture Content (OMC) during compaction reduces energy usage and construction costs, but materials often require adjustments upon delivery to reach OMC. The dry back phase is essential for efficiently allowing the pavement to release trapped moisture, preventing surface issues such as moisture resurfacing, aggregate punch-in, and premature stiffness loss in pavements. This study investigated temporal soil moisture (SM) variation within the compaction layer under varying environmental conditions such as physical soil temperature (<i>T</i><sub><i>soil</i></sub>), net radiation (<i>Rn</i>), initial SM, and bulk density (<InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="10706_2025_3293_Article_IEq1.gif" Format="GIF" Height="12" Rendition="HTML" Resolution="72" Type="Linedraw" Width="18" /> </InlineMediaObject> <EquationSource Format="TEX">\({\rho }_{b}\)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>ρ</mi> <mi>b</mi> </msub> </math></EquationSource> </InlineEquation>). A Long Short-Term Memory (LSTM) model was developed and compared with a traditional three-layer water balance model to predict SM variations. The LSTM model showed superior accuracy, with a maximum Root Mean Square Error of 0.20% for Volumetric Moisture Content and 0.12% for Gravimetric Moisture Content. Integrating environmental data and site-specific soil characteristics with a data-driven model significantly advances moisture management in road construction, enhancing compaction and pavement durability.</p>

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Soil Moisture Prediction in Pavement Layers Using LSTM Neural Networks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月23日

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  • Amir Tophel,
  • Thi Mai Nguyen,
  • Jeffrey P. Walker,
  • Jayantha Kodikara

摘要

Moisture content in geomaterials critically impacts road construction. Optimising the Optimum Moisture Content (OMC) during compaction reduces energy usage and construction costs, but materials often require adjustments upon delivery to reach OMC. The dry back phase is essential for efficiently allowing the pavement to release trapped moisture, preventing surface issues such as moisture resurfacing, aggregate punch-in, and premature stiffness loss in pavements. This study investigated temporal soil moisture (SM) variation within the compaction layer under varying environmental conditions such as physical soil temperature (Tsoil), net radiation (Rn), initial SM, and bulk density ( \({\rho }_{b}\) ρ b ). A Long Short-Term Memory (LSTM) model was developed and compared with a traditional three-layer water balance model to predict SM variations. The LSTM model showed superior accuracy, with a maximum Root Mean Square Error of 0.20% for Volumetric Moisture Content and 0.12% for Gravimetric Moisture Content. Integrating environmental data and site-specific soil characteristics with a data-driven model significantly advances moisture management in road construction, enhancing compaction and pavement durability.