Prediction of soil–water characteristic curve for plastic soils using PSO algorithm


Environmental Earth Sciences, vol.82, no.1, 2023 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 82 Issue: 1
  • Publication Date: 2023
  • Doi Number: 10.1007/s12665-022-10687-0
  • Journal Name: Environmental Earth Sciences
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, IBZ Online, PASCAL, Aerospace Database, Applied Science & Technology Source, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, CAB Abstracts, Compendex, Computer & Applied Sciences, Environment Index, Geobase, INSPEC, Pollution Abstracts, Veterinary Science Database, Civil Engineering Abstracts
  • Keywords: Fredlund–Xing equation, Particle Swarm Optimization, Soil Suction, Soil Water Characteristic Curve
  • Süleyman Demirel University Affiliated: Yes


© 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.Soil suction is a critical parameter that governs the stress–strain behavior of an unsaturated soil. Soil water characteristic curves (SWCCs) establish correlations between soil suction and soil moisture in terms of water content or saturation and they can be utilized for unsaturated soil parameters required for constitutive models. Several models have been developed in the literature to construct the SWCC; however their predictive accuracy is generally insufficient. One of the most often used models for constructing SWCC is the Fredlund–Xing equation, and empirical equations have been proposed in the literature to estimate the fitting parameters of this equation. The goal of this study was to improve the prediction accuracy of the Fredlund–Xing equation by calibrating the empirical equations that were proposed to predict fitting parameters using the Particle Swarm Optimization (PSO) algorithm. The data set for the optimization studies consists of the results of suction experiments performed on 46 plastic soil samples. Using empirical equations with calibrated coefficients, as indicated in the study, to estimate the fitting parameters of the Fredlund–Xing equation improves the estimation performance of the model.