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<p>Particle shape and gradation govern force chain networks and interparticle contacts in dense granular systems, thereby determining macroscopic shear strength. To investigate their influence on the peak internal friction angle (<i>φ</i><sub><i>d</i></sub>), triaxial tests were conducted on four binary mixed granular materials with contrasting morphologies, including Nanhai calcareous sand (GZS), Fujian standard sand (FS), glass beads (GB), and glass sand (GS). Ensemble learning algorithms were then employed to model <i>φ</i><sub><i>d</i></sub> from shape and gradation parameters. Results show that particle shape dominates <i>φ</i><sub><i>d</i></sub>: irregular particles yield systematically higher <i>φ</i><sub><i>d</i></sub>, whereas gradation plays a secondary role. Fine particle content (FC) induces non-monotonic effects. Angular particles (GZS, GS) exhibit an inverted V-shaped trend, with peak <i>φ</i><sub><i>d</i></sub> at a threshold FC. In contrast, subrounded particles (FS, GB) display a V-shaped trend, with minimum <i>φ</i><sub><i>d</i></sub> at the threshold FC. For subrounded particles, the threshold FC correlates strongly with the size ratio (SR). Heatmap analysis further identifies aspect ratio (AR) as the shape parameter most strongly correlated with <i>φ</i><sub><i>d</i></sub>. XGBoost and multilayer perceptron (MLP) models were developed to predict <i>φ</i><sub><i>d</i></sub> from shape and particle size inputs. Both models achieve good fit, confirming the relevance of the selected features. Due to its superior handling of small-sample data and multi-feature interactions, XGBoost significantly outperforms MLP in both accuracy and stability, offering a robust tool for predicting shear strength in granular materials.</p>

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Effects of particle shape and gradation on shear strength and AI-based prediction of friction angle

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月15日

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  • Zhaoyang Xu,
  • Zhongqi Tian,
  • Junyuan Wang,
  • Guangyu Li,
  • Lijun Ke

摘要

Particle shape and gradation govern force chain networks and interparticle contacts in dense granular systems, thereby determining macroscopic shear strength. To investigate their influence on the peak internal friction angle (φd), triaxial tests were conducted on four binary mixed granular materials with contrasting morphologies, including Nanhai calcareous sand (GZS), Fujian standard sand (FS), glass beads (GB), and glass sand (GS). Ensemble learning algorithms were then employed to model φd from shape and gradation parameters. Results show that particle shape dominates φd: irregular particles yield systematically higher φd, whereas gradation plays a secondary role. Fine particle content (FC) induces non-monotonic effects. Angular particles (GZS, GS) exhibit an inverted V-shaped trend, with peak φd at a threshold FC. In contrast, subrounded particles (FS, GB) display a V-shaped trend, with minimum φd at the threshold FC. For subrounded particles, the threshold FC correlates strongly with the size ratio (SR). Heatmap analysis further identifies aspect ratio (AR) as the shape parameter most strongly correlated with φd. XGBoost and multilayer perceptron (MLP) models were developed to predict φd from shape and particle size inputs. Both models achieve good fit, confirming the relevance of the selected features. Due to its superior handling of small-sample data and multi-feature interactions, XGBoost significantly outperforms MLP in both accuracy and stability, offering a robust tool for predicting shear strength in granular materials.