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A Semi-Empirical Method for Predicting Soil Void Ratio from CPTu Data via Soil Density Correlation

  • Xiang Meng
  • , Hongfei Duan*
  • , Mingyu Liu
  • , Gaoshan Li
  • , Zhongnian Yang
  • , Wei Shi
  • , Xianzhang Ling
  • *Corresponding author for this work
  • Qingdao University of Technology
  • China University of Mining & Technology, Beijing
  • Sun Yat-Sen University
  • Ltd.
  • School of Civil Engineering, Harbin Institute of Technology

Research output: Contribution to journalArticlepeer-review

Abstract

Soil void ratio is a key parameter in geotechnical engineering design and geological hazard prevention. However, existing methods for determining void ratio are plagued by issues such as difficulty in sampling, susceptibility of samples to disturbance, and heavy experimental workload. The cone penetration test, with its advantages of simple operation, high survey efficiency, and high accuracy, has gradually become a commonly used in situ testing method in engineering investigations. Based on data from the Yellow River Delta, this paper evaluates the applicability of several models related to void ratio. Combined with the Robertson density prediction model, a semi-empirical model for predicting void ratio based on the piezocone penetration test (CPTu), in situ testing is proposed, which enables efficient evaluation by establishing a conversion mechanism between soil density and void ratio. Verification using a database built from six types of nearly saturated sedimentary soil data shows that underestimation of predicted density will amplify the error of soil void ratio. The prediction accuracy is significantly improved after coefficient correction. Finally, a simple model for predicting void ratio that only requires CPTu data is developed, providing a sampling-free evaluation tool for estuarine and marine sedimentary areas.

Original languageEnglish
Article number9167
JournalApplied Sciences (Switzerland)
Volume15
Issue number16
DOIs
StatePublished - Aug 2025
Externally publishedYes

Keywords

  • CPTu
  • in situ prediction
  • soil density
  • void ratio

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