ORIGINAL RESEARCH article
Front. Earth Sci.
Sec. Geohazards and Georisks
This article is part of the Research TopicFailure Analysis and Risk Assessment of Natural Disasters Through Machine Learning and Numerical Simulation, volume VView all 13 articles
Data-driven identification of swell potential of clayey soils for engineering surveying using genetic projection pursuit
Provisionally accepted
- Guangzhou Urban Planning Survey and Design Institute, Guangzhou, China
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Abstract: Accurately classifying swell potential grades of clayey soils remains a significant challenge in geotechnical engineering, primarily due to the complex interplay of multiple factors that govern swelling-shrinkage behavior. To address this, this study develops a hybrid intelligent model by integrating a Genetic Algorithm (GA) with Projection Pursuit (PP). The GA optimizes the critical projection direction within the PP framework, transforming multi-dimensional soil indicator data into representative one-dimensional projection values. A logistic curve function is then employed to establish the nonlinear mapping between these optimized projection values and the corresponding empirical swelling-shrinkage grades, forming a GA-optimized Projection Pursuit (GAPP) discriminant model. Five key soil parameters-liquid limit, plasticity index, natural water content, free swell rate, and total swelling-shrinkage rate-were selected as the input indicators based on their mechanistic relevance. Model validation confirms its high predictive accuracy. Furthermore, application to a real-world highway engineering case demonstrates that the model's predictions align closely with observed field conditions, effectively verifying its practical feasibility and reliability. The proposed GAPP model thus provides a robust and effective methodological tool for the quantitative identification and grading of expansive soil swelling-shrinkage potential.
Keywords: Clayey soil, Genetic Algorithm, IDENTIFICATION, Projection pursuit, Swell potential
Received: 08 Jan 2026; Accepted: 06 Feb 2026.
Copyright: © 2026 Lou, Liang, Tan, Huang and Zhou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Zhu Liang
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