Geofluids (Jan 2022)

Establishment and Experimental Verification of Stress-Temperature Coupled Damage Model of Warm Frozen Soil

  • Shan Wei,
  • Yang Tao,
  • Guo Ying,
  • Xu Zhichao,
  • Zhang Chengcheng

DOI
https://doi.org/10.1155/2022/5101425
Journal volume & issue
Vol. 2022

Abstract

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Under the background of rising environmental temperature, the state of warm frozen soil near the phase transition is extremely unstable. In order to explore the relationship between temperature and mechanical properties of warm frozen soil, a damage model of warm frozen soil structure under the coupling of stress and temperature is established based on the strain equivalent theory of damage mechanics. Based on the Mohr-Coulomb criterion, the nominal stress is used to represent the stress damage of frozen soil elements, the initial elastic modulus is used to represent the temperature damage, and a composite damage factor is introduced to describe their coupled relationship. Through a triaxial compression experiment of frozen soil, the experimental data and stress-strain curve are obtained. The full-fitting method based on the experimental data (method 1) and the semitheoretical semifitting method based on the characteristic points of the stress-strain curve (method 2) are used to obtain the shape parameters and scale parameters of the stress-temperature coupled damage model corresponding to different fitting methods. Based on the triaxial compression tests of frozen sand and frozen silty clay, the reliability of the stress-temperature coupled damage model results obtained by the two parameter determination methods under the conditions of strain softening and strain hardening is verified. The results show that both methods are applicable under the condition of strain softening and strain hardening and method 2 is better than method 1 under the condition of strain softening. Compared with the prediction results of the single stress damage model, the stress-temperature coupled damage model can effectively reduce the influence of the parameter estimation error on the results and improve the overall stability of the model.