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<p>Characterizing the spatial distribution of soil types within complex subsurface environments remains a significant challenge in geotechnical engineering. This study proposes a novel methodology that integrates geostatistics with a data transformation procedure based on Piezocone Penetration Tests to systematically evaluate the spatial variability of soil behavior types in dredged sediments at a test site in Jiangsu, China. The transformation procedure estimates the marginal distribution of observations and converts non-Gaussian data into Gaussian form, enabling more robust and reliable quantification of prediction uncertainties. The soil behavior type index (<i>I</i><sub>c</sub>) is employed to quantitatively characterize the soil types, while spatial variability, including both magnitude and autocorrelation, is analyzed through trend removal, data transformation, and semivariogram modeling. Kriging interpolation, combined with back-transformation, is then used to estimate the mean, coefficient of variation, median, and confidence intervals of <i>I</i><sub>c</sub> at unsampled locations. Comparisons with independent validation soundings, as well as predictions generated without data transformation, confirm the enhanced accuracy, robustness, and reliability of the proposed methodology. Overall, the main contributions of this study are as follows: (1) the introduction of a methodological innovation that systematically incorporates data transformation into geostatistical analysis for soil behavior type prediction, and (2) the development of a generalizable framework for quantifying spatial uncertainties in complex sites, which provides practical guidance for site characterization beyond a single case study.</p>

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Predicting and mapping soil behavior types of dredged sediments using CPTU data and a hybrid geostatistics–data transformation method

  • 期刊论文

  • 发表日期：

    2026年1月8日

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  • Wei Duan,
  • Zening Zhao,
  • Guojun Cai,
  • Haifeng Zou,
  • Xidong Zhang,
  • Yifei Sun,
  • Shaoyun Pu,
  • Xiaoqiang Li,
  • Ya Chu,
  • Songyu Liu

摘要

Characterizing the spatial distribution of soil types within complex subsurface environments remains a significant challenge in geotechnical engineering. This study proposes a novel methodology that integrates geostatistics with a data transformation procedure based on Piezocone Penetration Tests to systematically evaluate the spatial variability of soil behavior types in dredged sediments at a test site in Jiangsu, China. The transformation procedure estimates the marginal distribution of observations and converts non-Gaussian data into Gaussian form, enabling more robust and reliable quantification of prediction uncertainties. The soil behavior type index (Ic) is employed to quantitatively characterize the soil types, while spatial variability, including both magnitude and autocorrelation, is analyzed through trend removal, data transformation, and semivariogram modeling. Kriging interpolation, combined with back-transformation, is then used to estimate the mean, coefficient of variation, median, and confidence intervals of Ic at unsampled locations. Comparisons with independent validation soundings, as well as predictions generated without data transformation, confirm the enhanced accuracy, robustness, and reliability of the proposed methodology. Overall, the main contributions of this study are as follows: (1) the introduction of a methodological innovation that systematically incorporates data transformation into geostatistical analysis for soil behavior type prediction, and (2) the development of a generalizable framework for quantifying spatial uncertainties in complex sites, which provides practical guidance for site characterization beyond a single case study.