Assessing the Reliability of Empirical Correlations for Compression Index Prediction in Fine-Grained Soils

Can we really trust traditional compression index correlations? Lessons from a global soil database

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.

EMPIRICAL CORRELATIONS BETWEEN THE COMPRESSION INDEX (Cc) AND SOIL PHYSICAL PROPERTIES OF CLAYS BELONGING TO THE QUATERNARY SEDIMENTS IN THU THIEM AREA, HO CHI MINH CITY NGUYEN NGOC PHUC

Settlement prediction is one of the most extensively studied problems in the field of geotechnical engineering. Analytical formulas for estimating soil compression and settlement that incorporate loading history through the compression index (Cc) often yield highly reliable results. Therefore, the compression index (Cc) is considered one of the key parameters in settlement prediction. When the results of the consolidation compression test is expressed as the relationship e ~ lgs' form, historical load-bearing parameters of the soil will be obtained, including: compression index Cc, swelling index Cs and pre-consolidation stress s'0. Estimating the ground settlement by considering the loading history using the CC compression index will give highly reliable prediction results. Therefore, the compression index CC is one of the important information. However, domestic research in this area remains limited. In practice, young sedimentary soils often undergo complex consolidation processes due to both natural and anthropogenic loading and unloading, which significantly alter their deformation characteristics. Consequently, developing empirical correlations between the compression index (Cc) and soil physical properties for young sediments in Vietnam is of particular necessity. This paper provides some empirical relationships on the compression index CC in clays of quaternary sediments in Thu Thiem, Ho Chi Minh City area.

Robust Evaluation Framework for Compression Index Prediction Models

Data-Driven Mechanical ROP Prediction: Construction and Validation of Committee Machine Model

With the continuous development of drilling technology, accurately predicting mechanical penetration rates is particularly important for improving operational efficiency and reducing costs. Existing methods often struggle to provide reliable predictions when faced with complex geological conditions and variable drilling environments because they primarily rely on traditional models and fail to adequately consider various influencing factors and their nonlinear relationships. To ad-dress these issues, this paper proposes a mechanical penetration rate prediction model based on committee machines. This model effectively captures the variability characteristics of mechanical penetration rates by integrating multiple expert models while employing wavelet filtering methods to denoise the data to enhance data quality. In the application case, this paper collects relevant drilling parameter data based on a vertical well in a specific block. The evaluation of the model shows that it performs excellently in key indicators such as mean square error, coefficient of determination, root mean square error, and mean absolute error, particularly demonstrating a high predictive capability and stability by explaining 97.19% of data variability. The advantage of the constructed model lies in its strong ensemble learning ability, which not only enhances the prediction accuracy of mechanical penetration rates but also helps to deepen the understanding of the dynamic changes in the drilling process, providing effective support for subsequent drilling optimization and resource development.

Quantifying parameter uncertainty in empirical correlations for soft clay characterisation

Preliminary estimation of the undrained shear strength of soft clays commonly relies on empirical or semi-empirical correlations incorporating soil index properties and stress history. In practical design situations where laboratory or field data are limited, such correlations are widely applied, although their reliability depends strongly on parameter uncertainty and inherent data variability. This study applies Global Sensitivity Analysis (GSA) to quantify how uncertainty in key parameters propagates through a semi-empirical undrained strength relation representative of Swedish practice. A combined Sobol variance-based analysis and Monte Carlo Filtering (MCF) approach is used to assess both parameter importance and model performance. Experimental datasets digitised from published Swedish investigations provide data-informed bounds for the input parameters. Results from both GSA methods consistently indicate that soil plasticity, represented by the liquid limit, is the dominant contributor to output variability and predictive performance. In contrast, the overconsolidation ratio and exponent parameter show limited influence. MCF further reveals that behavioural model responses are mainly associated with medium- to high-plasticity clays, corresponding to liquid limits of approximately 45-75%. The proposed framework offers a transparent basis for uncertainty assessment of empirical geotechnical correlations and provides practical insight for engineering design.

Comparative evaluation of sigmoidal models for particle size distribution of typical tropical urban soils

Particle size distribution (PSD) is a fundamental soil property that strongly influences hydraulic, mechanical, and deformation behavior in geotechnical engineering. Accurate mathematical representation of PSD curves is therefore meaningful for reliable soil characterization and parameter estimation. This study evaluates the fitting performance of three sigmoidal models—the Van Genuchten (1980) model, the Fredlund and Xing (1994) model, and a logistic model—for representing PSD curves of three typical soil types (clay, sandy clay, and clayey sand) in Go Vap Ward, Ho Chi Minh City, Vietnam. Laboratory particle size analyses were conducted using sieve and hydrometer tests in accordance with Vietnamese geotechnical standards. Model performance was assessed using the coefficient of determination (R²), root mean square error (RMSE) and sum of squared errors (SSE). The results show that all three sigmoidal models provide excellent agreement with experimental PSD data, with R² values ranging from 0.9616 to 0.9992. However, model performance varies depending on soil texture classes. The logistic model achieved the best fitting performance for clay soil, whereas the Fredlund and Xing model produced the lowest RMSE and SSE values for sandy clay and clayey sand. These findings highlight the importance of texture-dependent model selection and provide practical guidance for PSD modelling of tropical urban soils in geotechnical engineering applications.