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

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

Ahangar-Asr, A., Faramarzi, A., Mottaghifard, N. and Javadi, A.A. (2011) Modeling of permeability and compaction characteristics of soils using evolutionary polynomial regression. Computers & Geosciences, 37 (11). pp. 1860-1869. ISSN 0098-3004 (doi:10.1016/j.cageo.2011.04.015)

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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 arameters. 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.

Item Type: Article
Additional Information: [1] Available online 13 May 2011.
Uncontrolled Keywords: optimum moisture content, maximum dry density, permeability evolutionary computing, data mining
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Pre-2014 Departments: School of Engineering
Related URLs:
Last Modified: 14 Oct 2016 09:23
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
URI: http://gala.gre.ac.uk/id/eprint/9584

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