Constitutive Relationship Of Soils And Numerical Modeling

Soils are aggregates of mineral particles, and together with air and/or water in the void spaces they form three-phase systems. The constitutive relations of soils are influenced by many factors, and they are the basis of computer simulation of soil body, so many scholars have paid attention to investigation of constitutive relations of soils since 1960s.Many factors which influence constitutive relations of soils are summerized in this paper at first.And many constitutive models of soils are commentated.Then point out that it is a proper method to model constitutive relations by means of neural networks.Secondly, the neural networks of BP and RBF which can be applied for modeling constitutive relations of soil are introduced.Thirdly,the numerical modeling methods of constitutive relations of soil are researched,which is the major work of this paper.The triaxial tests of sand and mucky soil have been done.The sand used in triaxial test is graded one and the mucky soil is undisturbed .The test result shows that the test data are very discrete whenever the soil sample is made up or undisturbed.When the test data are discrete and the quantity of data is not enough,the neural network trained by conventional method will have lower accuracy or bring about over-fitting.These are primarily studied in this paper.One of the ways which is used to improve the precision of networks is to add new training samples by interpolation,which can take effect to a certain extend but is not always effective, moreover the stress range of the training samples should cover the possible stress range of actual engineering when the neural network models are applied in engineering;The second way is the method of normalization.Research shows that the stress-strain relationship of soil have the charactoristic of normalization. The triaxial test data are normalized by choosing proper normalization parameter. The neural networks of RBF are trained by regarding the normalized data as samples and then the ideal constitutive model of soil described by neural networks are obtained(include nonlinear elastic model and elastoplastic model). This way can reduce the interference of noise signal, avoid over-fitting of networks, lower the infuence caused by insufficiency and scatter of test data and can achieve probabilistic optimization automatically,furthermore,can reflect dilatancy and influence of stress path.The data samples are usually divided into two parts when neural networks are applied in fitting constitutive relations of soils.One part is used in training network,the other is used to compare with predictive value of network to test the generalization capability of network.But this method to be used in modeling constitutive relation is not very ideal because the test data of soil are usually insufficient and discrete.So another way to judge training success is given in this paper:1) There should be no overfitting by observing the visualization curved surfaces obtained from simulating;2) The simulation value on every test stress path should close to the test value.The reliability of network in the range of test stress will be guaranteed if the above-mentioned two points are satisfied.According to the way of judgement,the numerical models obtained by using normalization charactoristic of stress-strain relation of soil are successful.Fourthly, the FEM with constitutive model described by neural networks is analyzed;The elastroplastic matrix of soil based on generalized theory of plasticity is deduced; The neural network model of sand is brought into FEM program , then the calculation to the test sample is done and the results fit the experimental data well. Moreover the results show that the constitutive model adopted in FEM should reflect influence of stress path when soil body is computed.