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Experimental investigation on hydromechanical behavior of basalt and numerical modeling by return mapping algorithms

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Abstract

In order to investigate the hydromechanical behavior of basalt in Baihetan hydropower station in China, a series of uniaxial and triaxial hydromechanical tests were carried out in the laboratory. Experimental results indicated that the deformation state of samples transformed from compaction to dilation. Characteristic stresses were determined by a moving point regression technique, including the crack closure stress \(\sigma_{cc}\), crack initiation stress \(\sigma_{ci}\), crack damage stress \(\sigma_{cd}\), and peak stress \(\sigma_{p}\), revealing a strong dependence on the confining and pore pressures. Failure modes of the basalt changed from tensile to tensile-shear failure as the confining pressure increased. Moreover, scanning electron microscope (SEM) tests demonstrated that the growth of microcracks caused the failure of the basalt. Based on the experimental results, an elastoplastic constitutive model was proposed to take into account the hardening and softening, as well as the hydro-mechanical behavior of basalt. The return mapping algorithms of the elastoplastic constitutive model are deduced for plastic corrections. Model parameters were determined by experimental results. Finally, the validity of the model was demonstrated by comparing the experimental results with the simulated curves.

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The data used to support the findings of this study are available from the corresponding author upon request.

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Funding

This work was supported by the National Key R&D Program of China (No. 2018YFC0407004), the Natural Science Foundation of China (Grant Nos. 51939004, 11772118, and 11772116), the Postgraduate Research & Practice Innovation Program of Jiangsu Province (Grant No. KYCX22_0625), and the Qinglan Project and 111 Project.

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Correspondence to Weiya Xu.

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Zhang, T., Xu, W., Wang, H. et al. Experimental investigation on hydromechanical behavior of basalt and numerical modeling by return mapping algorithms. Bull Eng Geol Environ 82, 132 (2023). https://doi.org/10.1007/s10064-023-03139-x

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  • DOI: https://doi.org/10.1007/s10064-023-03139-x

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