Projects per year
Abstract
In geotechnical site investigation, Robertson’s soil behavior type (SBT) chart is widely used for soil classification based on two quantities measured during a cone penetration test (CPT), the normalized cone resistance Qt and the normalized friction ratio FR. Qt and FR are negatively correlated and provide complementary information for soil classification. However, this cross-correlation between Qt and FR has not been explicitly modelled in previous studies of subsurface soil classification and stratification using an often-limited number of CPT soundings from a specific site. This study aims to leverage such cross-correlation for improving CPT-based stratification and zonation by a joint sparse representation of Qt and FR in a vertical cross-section, as well as quantifying their uncertainty under a Bayesian framework. In addition, direct application of the SBT chart to a vertical cross-section often leads to noisy results (e.g., SBTs fluctuate rapidly and unrealistically within short distances). The noises are subsequently removed mainly by subjective engineering judgment in current practices. In this study, a randomization of input measurements is proposed to filter out the noise and improve computational efficiency simultaneously. Both simulated and real data examples are used to illustrate the proposed method. The results indicate that the proposed method significantly improves accuracy of the soil classification and stratification and automatically removes the noise.
© The Author(s)
| Original language | English |
|---|---|
| Pages (from-to) | 6537-6555 |
| Number of pages | 19 |
| Journal | Acta Geotechnica |
| Volume | 20 |
| Issue number | 12 |
| Online published | 16 Aug 2025 |
| DOIs | |
| Publication status | Published - Dec 2025 |
Bibliographical note
Publisher Copyright:© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.
Funding
The work described in this paper was supported by grants from the Research Grant Council of the Hong Kong Special Administrative Region (Project Nos: 11203322 and 11207724). The financial support is gratefully acknowledged.
Research Keywords
- Bayesian compressive sensing
- Data cross-correlation
- Joint sparse representation
- Noise removal
- Soil classification and stratification
- Uncertainty quantification
Publisher's Copyright Statement
- This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/
RGC Funding Information
- RGC-funded
Fingerprint
Dive into the research topics of 'Cone penetration test (CPT)-based soil classification and stratification with consideration of data cross-correlation and noises'. Together they form a unique fingerprint.Projects
- 2 Finished
-
GRF: Physics-informed and Interpretable Machine Learning of Reclamation-induced Consolidation of Soil Using Sparse Site Investigation and Field Monitoring Data
WANG, Y. (Principal Investigator / Project Coordinator) & LEE, S.-W. (Co-Investigator)
1/01/25 → 1/01/25
Project: Research
-
GRF: Smart Sensing of Subsurface Soil Liquefaction Potential Using Machine Learning and Virtual Reality
WANG, Y. (Principal Investigator / Project Coordinator) & STUEDLEIN, A. W. (Co-Investigator)
1/01/23 → 2/10/24
Project: Research
Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver