Modelling stress-strain and volume change behaviour of unsaturated soils using an evolutionary based data mining technique, an incremental approach

Javadi, A.A.; Ahangar-Asr, A.; Johari, A.; Faramarzi, A.; Toll, D.

Abstract

Modelling of unsaturated soils has been the subject of many research works in the past few decades. A number of constitutive models have been developed to describe the complex behaviour of unsaturated soils. However, many have proven to be unable to predict all aspects of the behaviour of unsaturated soils in a unified manner. In this paper an alternative new approach is presented, based on the Evolutionary Polynomial Regression (EPR) technique. EPR is a data mining technique that generates a transparent and structured representation of the behaviour of a system directly from input test data. The capabilities of the proposed EPR-based framework in modelling of behaviour of unsaturated soils are illustrated using results from a comprehensive set of triaxial tests on samples of compacted unsaturated soils from literature. The main parameters used for modelling of the behaviour of unsaturated soils during shearing are initial water content, initial dry density, mean net stress, axial strain, suction, volumetric strain, and deviator stress. The model developed is used to predict different aspects of the behaviour of unsaturated soils for conditions not used in the model building process. The results show that the proposed approach provides a useful framework for modelling of unsaturated soils. The merits and advantages of the proposed approach are highlighted.

Citation

Javadi, A., Ahangar-Asr, A., Johari, A., Faramarzi, A., & Toll, D. (2012). Modelling stress-strain and volume change behaviour of unsaturated soils using an evolutionary based data mining technique, an incremental approach. Engineering Applications of Artificial Intelligence, 25(5), 926-933. https://doi.org/10.1016/j.engappai.2012.03.006

• Journal Article Type: Article
• Publication Date: 2012-08
• Deposit Date: Aug 1, 2023
• Journal: Engineering Applications of Artificial Intelligence
• Print ISSN: 0952-1976
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 25
• Issue: 5
• Pages: 926-933
• DOI: https://doi.org/10.1016/j.engappai.2012.03.006