- Title
- A comprehensive approach to karst identification and groutability evaluation - a case study of the Dehou reservoir, SW China
- Creator
- Shangxin, Feng; Yufei, Zhao; Yujie, Wang; Shanyong, Wang; Ruilang, Cao
- Relation
- Engineering Geology Vol. 269, Issue May 2020, no. 105529
- Publisher Link
- http://dx.doi.org/10.1016/j.enggeo.2020.105529
- Publisher
- Elsevier
- Resource Type
- journal article
- Date
- 2020
- Description
- Yunnan Province in SW China has one of the most developed karst terrains in China and has been historically affected by sinkholes and other instability phenomena caused by unpredictable cavity formation and well-developed underground hydraulic conduits. During construction of a karst seepage control reservoir project (grouting of a 4.6-km long anti-seepage curtain) in Yunnan province, some typical detection methods (electromagnetic and water injection tests) failed to map the complex geo-structural karst zones clearly, resulting in considerable grout loss and excessive leakage during the grouting phase of this project. To deal with this limitation, a comprehensive approach, including a new drilling process monitoring system, borehole electrical resistivity tomography, borehole sonic logging, and water injection tests, is employed to identify the general characterization, geometry, and spatial distribution of karst zones along the anti-seepage curtain. Additionally, a deep belief network (DBN) model replaced the empirical relationships typically used to predict the grouting quantity for groutability evaluation in karstified areas. The DBN algorithm is trained by data collected using this comprehensive approach (rate of penetration, rock permeability, resistivity, and P-wave velocities) and two previous grouting quantities as input data. The lessons learned in this case study permit us to define an effective comprehensive approach for delineating the general characterization of karst zones between two boreholes and locations of potential conduits for grout flow through the borehole. The proposed DBN model can predict the grouting quantity for karst regions, and the accuracy of prediction is greatly affected by previous grouting quantity rather than the collected parameters.
- Subject
- karst; water injection test; borehole electrical resistivity tomography (ERT); drilling process moinitoring (DPM) system; deep belief network (DBN); grouting quantity
- Identifier
- http://hdl.handle.net/1959.13/1424315
- Identifier
- uon:38054
- Identifier
- ISSN:0013-7952
- Language
- eng
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