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An evolutionary approach to modelling compaction characteristics of soils

An evolutionary approach to modelling compaction characteristics of soils

Ahangar-Asr, Alireza, Faramarzi, Asaad, Javadi, Akbar and Mottaghifard, Nasim (2011) An evolutionary approach to modelling compaction characteristics of soils. In: Proceedings of the 19th UK National Conference of the Association for Computational Mechanics in Engineering. Heriot-Watt University, Edinburgh, Scotland, UK, pp. 45-48. ISBN 9780956595119

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Abstract

An Evolutionary approach is used for prediction of maximum dry density (MDD) and optimum moisture content (OMC) as functions of some physical properties of soil. Evolutionary polynomial regression (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 square 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 and compaction tests from literature. Standard Proctor 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 literature and the experimental data. Comparison of the results shows that the proposed models are highly accurate and robust in predicting compaction characteristics of soils. Results from sensitivity analysis indicate that the models trained from experimental data have been able to capture the 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 and optimum moisture content.

Item Type: Conference Proceedings
Title of Proceedings: Proceedings of the 19th UK National Conference of the Association for Computational Mechanics in Engineering
Additional Information: [1] This paper was first presented in the Geomechanics / Material Modelling Section of the 19th UK National Conference of the Association for Computational Mechanics in Engineering (ACME2011) held from 5-6 April 2011 at Heriot-Watt University in Edinburgh, Scotland, UK.
Uncontrolled Keywords: maximum dry density, optimum moisture content, evolutionary computing, data mining
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TP Chemical technology
Pre-2014 Departments: School of Engineering
School of Engineering > Department of Civil Engineering
Related URLs:
Last Modified: 14 Oct 2016 09:26
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
URI: http://gala.gre.ac.uk/id/eprint/11129

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