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<p>Particle size and shape are critical for characterizing gravel soils, typically quantified through the analysis of two-dimensional (2D) images and three-dimensional (3D) models. However, obtaining 3D particle features can be challenging and time-consuming, often resulting in low efficiency. To address this, many researchers have recently attempted to estimate 3D morphological features from 2D data by establishing relationships between 2D image features and their 3D counterparts. Nevertheless, these methods generally focus on specific particle categories within a limited region, limiting their broader applicability. In response, this study proposes a method for acquiring extensive morphological data for gravelly soils in both 2D and 3D formats through multiple collections. Additionally, it introduces and validates a practical approach for deriving 3D information from 2D image analysis, offering a series of new equations that are compared with previously published models. The result demonstrates that 3D morphological features, including particle size and shape, can be effectively estimated from 2D data using linear and polynomial correlation equations. </p>

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Quantitative correlation analysis of 2D and 3D particle morphology of gravelly soils

  • 期刊论文

  • 发表日期：

    2025年7月16日

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  • Jian Gong,
  • Zehong Wu,
  • Wenju Zhu,
  • Zongrui Tu,
  • Mingjie Jiang,
  • Guoxiong Mei

摘要

Particle size and shape are critical for characterizing gravel soils, typically quantified through the analysis of two-dimensional (2D) images and three-dimensional (3D) models. However, obtaining 3D particle features can be challenging and time-consuming, often resulting in low efficiency. To address this, many researchers have recently attempted to estimate 3D morphological features from 2D data by establishing relationships between 2D image features and their 3D counterparts. Nevertheless, these methods generally focus on specific particle categories within a limited region, limiting their broader applicability. In response, this study proposes a method for acquiring extensive morphological data for gravelly soils in both 2D and 3D formats through multiple collections. Additionally, it introduces and validates a practical approach for deriving 3D information from 2D image analysis, offering a series of new equations that are compared with previously published models. The result demonstrates that 3D morphological features, including particle size and shape, can be effectively estimated from 2D data using linear and polynomial correlation equations.