Modeling of permeability and compaction characteristics of soils using evolutionary polynomial regression

Ahangar-Asr, A.; Faramarzi, A.; Mottaghifard, N.; Javadi, A.A.

Abstract

This paper presents a new approach, based on evolutionary polynomial regression (EPR), for prediction of permeability (K), maximum dry density (MDD), and optimum moisture content (OMC) as functions of some physical properties of soil. EPR is a data-driven method based on evolutionary computing aimed to search for polynomial structures representing a system. In this technique, a combination of the genetic algorithm (GA) and the least-squares method is used to find feasible structures and the appropriate parameters of those structures. EPR models are developed based on results from a series of classification, compaction, and permeability tests from the literature. The tests included standard Proctor tests, constant head permeability tests, and falling head permeability tests conducted on soils made of four components, bentonite, limestone dust, sand, and gravel, mixed in different proportions. The results of the EPR model predictions are compared with those of a neural network model, a correlation equation from the literature, and the experimental data. Comparison of the results shows that the proposed models are highly accurate and robust in predicting permeability and compaction characteristics of soils. Results from sensitivity analysis indicate that the models trained from experimental data have been able to capture many physical relationships between soil parameters. The proposed models are also able to represent the degree to which individual contributing parameters affect the maximum dry density, optimum moisture content, and permeability.

Citation

Ahangar-Asr, A., Faramarzi, A., Mottaghifard, N., & Javadi, A. (2011). Modeling of permeability and compaction characteristics of soils using evolutionary polynomial regression. Computers and Geosciences, 37(11), 1860-1869. https://doi.org/10.1016/j.cageo.2011.04.015

• Journal Article Type: Article
• Publication Date: 2011-11
• Deposit Date: Aug 1, 2023
• Journal: Computers and Geosciences
• Print ISSN: 0098-3004
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 37
• Issue: 11
• Pages: 1860-1869
• DOI: https://doi.org/10.1016/j.cageo.2011.04.015