Statistical damage constitutive model for rocks considering temperature effects and its validation
In order to predict and evaluate the stability of deep high-temperature rock masses,theoretical derivation methods were used to study the constitutive relationship of rocks under(after)temperature action.Firstly,based on existing rock statistical damage constitutive models,the influence of temperature on rock damage variables was considered,and parameters such as damage variable correction coefficient,damage initiation stress,and initiation strain were introduced.Secondly,it is assumed that the strength of the microelements follows a power function distribution and conforms to the Hoek-Brown(H-B)strength criterion.In response to the drawback that statistical damage constitutive models cannot reflect the strong prepeak microcrack compaction effect,corresponding model correction coefficients were introduced.Thirdly,a rock statistical damage constitutive model considering temperature effect was established,and the expression of model parameters was determined.Finally,the accuracy of the model was verified by comparing the data obtained from uniaxial and conventional triaxial compression tests on granite under different temperature conditions with the calculation results of the model in the literature.The results show that the calculation results of the proposed model are consistent with the experimental results in literature in terms of numerical values,distribution patterns and trends.For each stage of the whole process σ-ε curve,the calculation result of the model in this paper is better than that of the constitutive model in the literature,and fits with the test curve better,which can better reflect the damage constitutive characteristics of rock under(after)high temperature.The parameters of the model are conventional and suitable for rocks under various temperature-pressure combination conditions.
rock mechanicspower functiontemperatureHoek-Brown(H-B)strength criterionmicrocrack compaction effectconstitutive model
万方数据
维普
