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<p>This study developed a regression-based model to predict shear wave velocity (Vs) for compaction assessment in kaolin–sand mixtures with different fines contents. Four mixtures (K100, K70, K50, K30) were compacted using the Standard Proctor method to determine maximum dry density (MDD) and optimum moisture content (OMC). A total of 16 specimens (of 4 Sets Soil mixtures) were tested, and Vs was measured using Bender Element (BE) testing with an improved clay–foam coupling interface to enhance signal clarity. Results showed that Vs increased with dry density and decreased with void ratio and porosity, reflecting its sensitivity to soil stiffness and particle contact conditions. A positive correlation between Vs and MDD and an inverse relationship with OMC were observed. A multiple linear regression model incorporating of different 16 types of fines content and dry density achieved strong predictive performance (R² = 0.9207). Compared to conventional density-based regression and machine learning approaches, the proposed model provides a physically interpretable relationship between soil composition and stiffness. The model is applicable to controlled fine–coarse mixtures and demonstrates the potential of Vs as a non-destructive parameter for compaction assessment. However, it remains preliminary and requires validation on various natural soils and under field conditions.</p>

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Regression Model for Soil Compaction Assessment via Shear Wave Velocity in Kaolin-sand Mixtures

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月13日

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  • Nur Ain Abu Bakar,
  • Mohd Khaidir Abu Talib,
  • Aziman Madun,
  • Mohd Firdaus Md Dan Azlan,
  • Muhammad Nur Hidayat Zahari

摘要

This study developed a regression-based model to predict shear wave velocity (Vs) for compaction assessment in kaolin–sand mixtures with different fines contents. Four mixtures (K100, K70, K50, K30) were compacted using the Standard Proctor method to determine maximum dry density (MDD) and optimum moisture content (OMC). A total of 16 specimens (of 4 Sets Soil mixtures) were tested, and Vs was measured using Bender Element (BE) testing with an improved clay–foam coupling interface to enhance signal clarity. Results showed that Vs increased with dry density and decreased with void ratio and porosity, reflecting its sensitivity to soil stiffness and particle contact conditions. A positive correlation between Vs and MDD and an inverse relationship with OMC were observed. A multiple linear regression model incorporating of different 16 types of fines content and dry density achieved strong predictive performance (R² = 0.9207). Compared to conventional density-based regression and machine learning approaches, the proposed model provides a physically interpretable relationship between soil composition and stiffness. The model is applicable to controlled fine–coarse mixtures and demonstrates the potential of Vs as a non-destructive parameter for compaction assessment. However, it remains preliminary and requires validation on various natural soils and under field conditions.