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Mechanical behavior of fine-grained sandstone in triaxial compression and elastoplastic modeling by return mapping algorithms

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Abstract

The study is devoted to the strength and deformation behavior of fine-grained sandstone. A series of triaxial compression tests were conducted on rock samples under various confining pressures (3, 5, 10, 15, and 20 MPa) in the laboratory. The experimental results showed that the nonlinear behavior and plastic deformation of the sandstone are prominent during loading process. The peak strength of the sandstone can be enhanced with the increase of the confining pressure. The initial yield stresses σy are approximately 87.2%, 80.2%, 62.1%, 75.2%, and 83.8% of the peak strengths σ p , respectively. The shear failure is predominated failure mode under different confining pressures. Based on experimental results, an elastoplastic model is proposed using a linear yield function in p-q plane, as well as a non-associated plastic potential function. Furthermore, the numerical formulations of a return mapping algorithm for constitutive model implementation are introduced in this work. The numerical results agree well with the experimental data, which show that this model is able to describe the elastoplastic behavior of the sandstone under triaxial compression.

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Acknowledgements

This work presented in this paper was financially supported by the National Natural Science Foundation of China (Grant Nos. 51479049, 11672343).

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Authors and Affiliations

  1. Key Laboratory of Ministry of Education for Geomechanics and Embankment Engineering, Hohai University, Nanjing, Jiangsu, 210098, China

    Susheng Wang, Weiya Xu, Chaojun Jia & Wei Wang

  2. Institute of Geotechnical Engineering, Hohai University, Nanjing, Jiangsu, 210098, China

    Susheng Wang, Weiya Xu, Chaojun Jia & Wei Wang

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  1. Susheng Wang
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  2. Weiya Xu
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  3. Chaojun Jia
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  4. Wei Wang
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Correspondence to Chaojun Jia.

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Wang, S., Xu, W., Jia, C. et al. Mechanical behavior of fine-grained sandstone in triaxial compression and elastoplastic modeling by return mapping algorithms. Bull Eng Geol Environ 77, 1689–1699 (2018). https://doi.org/10.1007/s10064-017-1094-4

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  • DOI: https://doi.org/10.1007/s10064-017-1094-4

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