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Zhang, Y., Liu, X., Yuan, S., Song, J., Chen, W., & Dias, D. (2023). A two-dimensional experimental study of active progressive failure of deeply buried Qanat tunnels in sandy ground. Soils and Foundations, 63(3), 101323.
Abstract: As an ancient underground hydraulic engineering facility, the Qanat system has been used to draw groundwater from arid regions. A qanat is a horizontal tunnel with a slight incline that draws groundwater from a higher location and delivers it to lower agricultural land. During long-term water delivery, the qanat tunnel has experienced different degrees of aging and collapse, which may result in the significant ground settlement and even disasters. This paper developed a two-dimensional laboratory system to investigate the influence of progressive failure on the stability of deeply buried qanat tunnels. The developed system is fully instrumented with a particle image velocimetry (PIV) system and earth pressure and displacement monitoring. A special cylindrical membrane tube is designed and connected to an advanced pressure–volume controller to simulate the step-wise failure process of the tunnel. Three model tests were conducted on a dry sand considering the buried qanat tunnels at three different depths. Experimental results clearly show the progressive evolution of soil arching effect in the dry sand associated with the progressive failure of the tunnels. The failure of the Qanat ground starts from the vault and develops upwards, which is closely related to the evolution of stress contour at three consecutive stages. Ground surface settlement and volume loss corresponding to three burial depths were compared. A deeply buried qanat tunnel has a small effect on surface settlement. Earth pressure evolution on the 2D plane shows the load redistribution when the qanat collapses. The maximum arch and the initial point of the limit state correspond to a volume loss of 12.5 % and 50 %, respectively. For the collapse of the deep buried qanat tunnel, ground earth pressure evolution can be divided into a stress-increasing region, stress-decreasing region, and no redistribution region. Furthermore, a multi trap-door model considering soil expansion is proposed to describe the progressive failure behavior and its effects.
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Wang, B., Luo, Y., Qian, J. -zhong, Liu, J. -hui, Li, X., Zhang, Y. -hong, et al. (2023). Machine learning–based optimal design of the in-situ leaching process parameter (ISLPP) for the acid in-situ leaching of uranium. Journal of Hydrology, 626, 130234.
Abstract: The migration process of leached uranium in the in-situ leaching of uranium is considered a typical reactive transport problem. During this process, the lixiviant concentration and injection rate are important in-situ leaching process parameters (ISLPP) to efficiently recover uranium. However, several uncertain factors affect the outcomes of the ISLPP design. In addition, the repeated use of the reactive transport model (RTM) for investigating the acid in-situ leaching of uranium with the application of the Monte Carlo method leads to a substantial computational load. For this reason, a machine learning (ML)–based surrogate model was developed with the backpropagation neural network (BPNN) method to replace the RTM under the condition of uncertain parameters. Moreover, the simulated annealing optimisation model for ISLPP was created based on the proposed surrogate model. The optimal ISLPP was achieved that generated maximum profits from uranium recovery under different lixiviant prices, uranium prices and exploitation times. The optimal design framework of ISLPP based on the proposed ML algorithm was then applied in the Bayan-Uul sandstone-type uranium deposit in Inner Mongolia, China. From our analysis, it was demonstrated that the ML-based surrogate model exhibited great fitness with the RTM. The optimal results of the ISLPP indicated that the lixiviant concentration and injection rate could be adjusted based on the fluctuations in lixiviant price, uranium price and exploitation time. If the prices of sulphuric acid were high, a specific concentration of hydrogen peroxide could be injected into the injection well to promote the oxidation and dissolution of the uranium ore to increase the income from the uranium recovery. The optimisation model can also use the ISLPP scheme to boost the revenues from different lixiviant prices, uranium prices and exploitation times under the uncertainty of porosity, illustrating the applicability of the ML-based optimal design method of ISLPP in ISL mining. A general framework for developing surrogate models, as well as for conducting uncertainty analyses for a wide range of groundwater models was proposed here yielding valuable insights.
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