| Issue |
E3S Web Conf.
Volume 730, 2026
International Conference on Advances and Innovations in Soft Soil Engineering (Soft Soils 2026)
|
|
|---|---|---|
| Article Number | 03001 | |
| Number of page(s) | 7 | |
| Section | Constitutive, Numerical, and Machine Learning Models | |
| DOI | https://doi.org/10.1051/e3sconf/202673003001 | |
| Published online | 03 August 2026 | |
Can we really trust traditional compression index correlations? Lessons from a global soil database
1 Departamento de Ingeniería Civil, Universidad de Alicante, Alicante, Spain
2 CDM Smith SE, Bochum, Germany
3 Dipartimento di Ingegneria Civile e Ambientale, Universitá degli Studi di Firenze, Firenze, Italy
* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.
Abstract
The compression index (Cc) is a key parameter for predicting consolidation settlement in fine-grained soils. In geotechnical practice, empirical correlations based on simple index properties are widely used as a fast and economical alternative to laboratory oedometer tests. However, most existing formulations were developed under site-specific conditions, raising concerns about their reliability when applied beyond their original scope. In this study, the predictive performance of 20 widely used empirical correlations for Cc estimation is evaluated using a comprehensive global database comprising 1008 soil samples compiled from multiple countries and geological settings worldwide. The dataset spans wide ranges of liquid limit (17.1–199.0%), plasticity index (2.0–82.0%), initial void ratio (0.279–7.114), natural water content (8– 244.1%), and Cc (0.013–2.2). Correlations are assessed for the complete dataset and for different compressibility ranges using multiple performance metrics, with Theil’s inequality coefficient adopted as the main ranking criterion. Results show that most traditional correlations exhibit large prediction errors and high dispersion when applied globally, particularly for low-compressibility soils. A simple correlation recently proposed by the authors, based solely on natural water content, demonstrates competitive performance, negligible bias, and improved robustness, offering a practical alternative for preliminary Cc estimation in data-scarce conditions.
© The Authors, published by EDP Sciences, 2026
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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