• Journal of the Korean Geotechnical Society
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• v.22 no.2
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• pp.41-53
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• 2006

The brittle materials like rocks show complicated strain-softening behavior after the peak which is hard to model using the classical constitutive models based on the relation between strain and stress tensors. A kinematically constrained three-dimensional microplane constitutive model is developed for granite. The model is verified by fitting the experimented data of Westerly granite and Bonnet granite. The triaxial behavior of granite is well reproduced by the model as well as the uniaxial behavior. We studied the development of the fracture zone in granite during blasting impact using the model with the standard finite element method. All the results obtained from the microplane model developed are compared to those from the linear elasticity model which is commonly used in many researches and practices. It is found that the nonlinearity of rocks sigificantly affects the results of analysis.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.5
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• pp.487-496
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• 2000

In this paper, the Multi-FNN(Fuzzy-Neural Networks) model is identified and optimized using HCM(Hard C-Means) clustering method and genetic algorithms. The proposed Multi-FNN is based on Yamakawa's FNN and uses simplified inference as fuzzy inference method and error back propagation algorithm as learning rules. We use a HCM clustering and Genetic Algorithms(GAs) to identify both the structure and the parameters of a Multi-FNN model. Here, HCM clustering method, which is carried out for the process data preprocessing of system modeling, is utilized to determine the structure of Multi-FNN according to the divisions of input-output space using I/O process data. Also, the parameters of Multi-FNN model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. A aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. The aggregate performance index stands for an aggregate objective function with a weighting factor to consider a mutual balance and dependency between approximation and predictive abilities. According to the selection and adjustment of a weighting factor of this aggregate abjective function which depends on the number of data and a certain degree of nonlinearity, we show that it is available and effective to design an optimal Multi-FNN model. To evaluate the performance of the proposed model, we use the time series data for gas furnace and the numerical data of nonlinear function.