EPR-based material modelling of soils considering volume changes

In this paper an approach is presented for developing material models for soils based on evolutionary polynomial regression (EPR), taking into account its volumetric behaviour. EPR is a recently developed hybrid data mining technique that searches for structured mathematical equations (representing the behaviour of a system) using genetic algorithm and the least squares method. Stress-strain data from triaxial test are used to train and develop EPR-based material models for soil. The developed models are compared with some of the well known conventional material models. In particular, the capability of the developed EPR models in predicting volume change behaviour of soils is illustrated. It is also shown that the developed EPR-based material models can be incorporated in finite element (FE) analysis. Two geotechnical examples are presented to verify the developed EPR-based FE model (EPR-FEM). The results of the EPR-FEM are compared with those of a standard FEM where conventional constitutive models are used to describe the material behaviour. The results show that EPR-FEM can be successfully employed to analyse geotechnical engineering problems. The advantages of the proposed EPR models are highlighted.

[1]  Ulrich Hermann,et al.  Sensitivity Analysis of Neural Networks in Spool Fabrication Productivity Studies , 2001 .

[2]  M. A. Crisfield,et al.  Finite elements in plasticity—theory and practice, D. R. J. Owen and E. Hinton, Pineridge Press, Swansea , 1981 .

[3]  D. Savić,et al.  A symbolic data-driven technique based on evolutionary polynomial regression , 2006 .

[4]  Xia-Ting Feng,et al.  Coupling recognition of the structure and parameters of non‐linear constitutive material models using hybrid evolutionary algorithms , 2004 .

[5]  Jamshid Ghaboussi,et al.  Autoprogressive training of neural network constitutive models , 1998 .

[6]  K. Lo,et al.  Performance of a Three-Dimensional Hvorslev–Modified Cam Clay Model for Overconsolidated Clay , 2004 .

[7]  Dayakar Penumadu,et al.  Triaxial compression behavior of sand and gravel using artificial neural networks (ANN) , 1999 .

[8]  A. Sherif El-Gizawy,et al.  Incorporating Neural Network Material Models within Finite Element Analysis for Rheological Behavior Prediction , 2007 .

[9]  Orazio Giustolisi,et al.  Scour depth modelling by a multi-objective evolutionary paradigm , 2011, Environ. Model. Softw..

[10]  Hoan-Kee Kim,et al.  Nonlinear constitutive models for FRP composites using artificial neural networks , 2007 .

[11]  D. Wood.  Soil Behaviour and Critical State Soil Mechanics , 1991 .

[12]  D. Savić,et al.  Advances in data-driven analyses and modelling using EPR-MOGA. , 2009 .

[13]  Yacoub M. Najjar,et al.  Simulating the stress–strain behavior of Georgia kaolin via recurrent neuronet approach , 2007 .

[14]  Akbar A. Javadi,et al.  Intelligent Finite Element Method and Application to Simulation of Behavior of Soils under Cyclic Loading , 2009, Foundations of Computational Intelligence.

[15]  Orazio Giustolisi,et al.  An investigation on stream temperature analysis based on evolutionary computing , 2008 .

[16]  L. Laloui,et al.  Experimental study of thermal effects on the mechanical behaviour of a clay , 2004 .

[17]  Akbar A. Javadi,et al.  Applications of artificial intelligence and data mining techniques in soil modeling , 2009 .

[18]  James H. Garrett,et al.  Knowledge-Based Modeling of Material Behavior with Neural Networks , 1992 .

[19]  Tomonari Furukawa,et al.  Accurate cyclic plastic analysis using a neural network material model , 2004 .

[20]  Akbar A. Javadi,et al.  Numerical implementation of EPR-based material models in finite element analysis , 2013 .

[21]  Akbar A. Javadi,et al.  Intelligent finite element method: An evolutionary approach to constitutive modeling , 2009, Adv. Eng. Informatics.

[22]  J H Garrett,et al.  KNOWLEDGE-BASED MODELLING OF MATERIAL BEHAVIOUR WITH NEURAL NETWORKS , 1991 .

[23]  Hai-Sui Yu,et al.  CASM: a unified state parameter model for clay and sand , 1998 .

[24]  Antonio Gens,et al.  Critical state models in computational geomechanics , 1988 .