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<p>This study presents a simplified sigmoidal model integrated with the Master Curve approach to estimate the resilient modulus (M<sub>R</sub>) of unbound granular materials (UGMs). M<sub>R</sub> measurements under varying moisture conditions were used to construct the Master Curve, with the optimum moisture content (OMC) adopted as the reference condition. Laboratory testing evaluated the influence of moisture on UGM resilient behaviour at three moisture levels: 0.75 OMC (dry of optimum), OMC, and 1.15 OMC (wet of optimum). The measured M<sub>R</sub> values were normalised and horizontally shifted along the bulk-stress axis to develop a unified, moisture-independent Master Curve that represents material behaviour across different stress and moisture states. Six UGM types were analysed to assess the model’s robustness and predictive capability, with a unique sigmoidal function developed for each material. The resulting Master Curves demonstrated strong predictive performance, achieving an average coefficient of determination (R<sup>2</sup>) of 0.86. Comparative analysis showed that the proposed sigmoidal model outperformed conventional M<sub>R</sub> prediction models in both accuracy and reliability across diverse materials and moisture conditions, demonstrating its suitability for pavement engineering applications.</p>

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Development of Master Curve to Characterise Resilient Modulus of Unbound Granular Materials

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月4日

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  • Gabriel Rugabandana,
  • Joseph Anochie-Boateng,
  • James Maina,
  • Siya Rimoy

摘要

This study presents a simplified sigmoidal model integrated with the Master Curve approach to estimate the resilient modulus (MR) of unbound granular materials (UGMs). MR measurements under varying moisture conditions were used to construct the Master Curve, with the optimum moisture content (OMC) adopted as the reference condition. Laboratory testing evaluated the influence of moisture on UGM resilient behaviour at three moisture levels: 0.75 OMC (dry of optimum), OMC, and 1.15 OMC (wet of optimum). The measured MR values were normalised and horizontally shifted along the bulk-stress axis to develop a unified, moisture-independent Master Curve that represents material behaviour across different stress and moisture states. Six UGM types were analysed to assess the model’s robustness and predictive capability, with a unique sigmoidal function developed for each material. The resulting Master Curves demonstrated strong predictive performance, achieving an average coefficient of determination (R2) of 0.86. Comparative analysis showed that the proposed sigmoidal model outperformed conventional MR prediction models in both accuracy and reliability across diverse materials and moisture conditions, demonstrating its suitability for pavement engineering applications.