Constitutive Model Identification of Rock Based on Artificial Intelligence
The Identification of rock constitutive model is a typical nonlinear system problem; introduction of the intelligent methods has greatly stimulated the research. In this paper, genetic algorithm , BP neural network and genetic programming are used in the identification of constitutive model ,the capacity of three intelligent methods in the model identification is compared ,whose results show that genetic algorithm can largely reduce the probability of trapping into localized optimum solution by means of its global searching ability ,however , because its pre-assumption of constitutive model has already lead to some error ,its fitting results are not so good . BP neural network and genetic programming need not pre-assume the structure of constitutive model ,the nonlinear mapping expressions between stress and strain are obtained by self-organization and self-learning .But ,the determination of BP neural network structure is experience-depending and time-consuming . In addition, genetic programming need not define its structure, so long as the functional set and terminal set are chosen, after series of genetic operations, the nonlinear relations between variables are outputted, therefore, the operation of genetic programming is simple which realize the visualization of the constitutive equations.
Qing Yang, Li Hua Zhu, Jing Jing He, Zeng Feng Yan and Rui Ren
S. J. Sha et al., "Constitutive Model Identification of Rock Based on Artificial Intelligence", Advanced Materials Research, Vols. 368-373, pp. 2509-2516, 2012