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<p>Establishing correlations between Cone Penetration Test (CPT) and Standard Penetration Test (SPT) data is essential in geotechnical engineering, as it enables accurate estimation of soil properties, streamlines site investigations, and enhances decision-making in foundation design and soil liquefaction evaluation. However, traditional models often overlook regional variability and uncertainties, limiting their applicability across diverse geological conditions. This study presents a Hierarchical Bayesian Modeling (HBM) framework to develop region-specific correlations for cohesionless soils, addressing these limitations, using a dataset of 581 paired CPT-SPT observations from eight global regions. The HBM approach explicitly incorporates regional differences and data variability using the Markov Chain Monte Carlo (MCMC) algorithm. Comparative analysis with conventional Bayesian models and existing correlations showed that the HBM significantly enhanced prediction accuracy and reliability. By accounting for site-specific variability and incorporating uncertainty, the HBM framework produces more robust and precise CPT-SPT correlations tailored to individual regions. This advancement provides substantial practical benefits for geotechnical engineering projects, particularly in soil property assessment and design.</p>

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Region-Specific CPT-SPT Correlations for Cohesionless Soils: A Hierarchical Bayesian Approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月15日

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  • Laith Sadik,
  • Sara Khoshnevisan

摘要

Establishing correlations between Cone Penetration Test (CPT) and Standard Penetration Test (SPT) data is essential in geotechnical engineering, as it enables accurate estimation of soil properties, streamlines site investigations, and enhances decision-making in foundation design and soil liquefaction evaluation. However, traditional models often overlook regional variability and uncertainties, limiting their applicability across diverse geological conditions. This study presents a Hierarchical Bayesian Modeling (HBM) framework to develop region-specific correlations for cohesionless soils, addressing these limitations, using a dataset of 581 paired CPT-SPT observations from eight global regions. The HBM approach explicitly incorporates regional differences and data variability using the Markov Chain Monte Carlo (MCMC) algorithm. Comparative analysis with conventional Bayesian models and existing correlations showed that the HBM significantly enhanced prediction accuracy and reliability. By accounting for site-specific variability and incorporating uncertainty, the HBM framework produces more robust and precise CPT-SPT correlations tailored to individual regions. This advancement provides substantial practical benefits for geotechnical engineering projects, particularly in soil property assessment and design.