A generalized formula for predicting soil compression index using multi-evolutionary algorithm

Khanh Pham

doi:10.1016/j.enggeo.2024.107789

2024-12-01

PRE

DOI：10.1016/j.enggeo.2024.107789

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摘要

En 中文

Correlation between soil compression index (Cc) and state parameters is frequently referenced in studies investigating the fundamental mechanisms underlying changes in soil compressibility. However, developing an efficient formula for Ccthat adequately captures the complexity of soil compressive behavior has been challenging for conventional approaches. This study utilized contemporary symbolic regression (SR) to propose a generalized formula for Ccthat can represent the nonlinear relationships with state parameters across various soil types. A geological database from southern Vietnam was utilized to establish this data-driven formula. Data exploration revealed the apparent combined effects of moisture content (w), initial void ratio (e0), and moist density (rho) on soil compressive behavior. Statistical indicators and graphical analysis were adopted to comprehensively assess the performance of the proposed formula against empirical equations found in the literature, aiming to gain a deeper understanding of the mechanism influencing changes in soil compressibility. The evaluation results demonstrated the efficiency of the proposed formula in predicting Cc, as evidenced by low error metrics and a good balance between precision and accuracy. Moreover, the applicability and limitations of the proposed formula were examined using different regional soils with specified geologic origins. Given its reliability and adequacy, the proposed formula explicitly quantified the nonlinear combined effects of e0, rho and w on the compressibility of undisturbed soils. However, further research accounting for clay minerals, specimen preparation, and geologic origins is needed to enhance the universal applicability of our understanding of soil compressive behavior.

Keyword:

Compressibility

Compression index

Symbolic regression

Parameter predictions