Advanced Search

Indexed by SCI、CA、РЖ、PA、CSA、ZR、etc .

Volume 35 Issue 4
Aug 2024
Turn off MathJax
Article Contents
Lu Guo, Keqiang He, Honghua Liu, Fandi Meng, Xuchun Wang. Physical Prediction Model of Compound Hydrodynamic Unload-Load Response Ratio and Its Application in Reservoir Colluvium Landslide. Journal of Earth Science, 2024, 35(4): 1304-1315. doi: 10.1007/s12583-022-1662-9
Citation: Lu Guo, Keqiang He, Honghua Liu, Fandi Meng, Xuchun Wang. Physical Prediction Model of Compound Hydrodynamic Unload-Load Response Ratio and Its Application in Reservoir Colluvium Landslide. Journal of Earth Science, 2024, 35(4): 1304-1315. doi: 10.1007/s12583-022-1662-9

Physical Prediction Model of Compound Hydrodynamic Unload-Load Response Ratio and Its Application in Reservoir Colluvium Landslide

doi: 10.1007/s12583-022-1662-9
More Information
  • Corresponding author: Keqiang He, keqianghe@sina.com
  • Received Date: 28 Dec 2021
  • Accepted Date: 28 Mar 2022
  • Issue Publish Date: 30 Aug 2024
  • It is well known that the deformation and damage of reservoir colluvium landslides are often determined by the combined dynamics of reservoir water level change and rainfall. Based on the systematic analysis of the change law of reservoir water level, rainfall and displacements of reservoir colluvium landslide, this paper proposes the compound hydrodynamic action of rainfall and reservoir water as the unload-load parameter, and the landslide displacement as the unload-load response parameter. Based on this, a physical prediction model of the compound hydrodynamic unload-load response ratio of reservoir colluvium landslide was established, and the quantitative relationship between the compound hydrodynamic unload-load response ratio and its stability evolution was in-depth analyzed and determined. On the basis of the above research, taking Shuping landslide, a typical hydrodynamic pressure landslide as an example, the unload-load response ratio model is used to systematically evaluate and predict the stability evolution law and the change trend of the landslide under compound hydrodynamic action. The prediction result shows that the variation law of the compound hydrodynamic unload-load response ratio is consistent with the dynamic evolution law of its stability. Therefore, the above studies show that the compound hydrodynamic unload-load response ratio parameter is an effective displacement dynamic evaluation parameter for reservoir colluvium landslides, so it can be used in the prediction of the reservoir colluvium landslides.

     

  • Conflict of Interest
    The authors declare that they have no conflict of interest.
  • loading
  • Carlà, T., Intrieri, E., Di Traglia, F., et al., 2017. Guidelines on the Use of Inverse Velocity Method as a Tool for Setting Alarm Thresholds and Forecasting Landslides and Structure Collapses. Landslides, 14(2): 517–534. https://doi.org/10.1007/s10346-016-0731-5
    Dai, T. F., Yi, Q. L., Hu, D. R., et al., 2014. The Application of Loading Unloading Response Ratio Theory to Stability Analysis of a Reservoir Type Landslide. Journal of China Three Gorges University (Natural Science), 36(1): 25–28. https://doi.org/10.13393/j.cnki.issn.1672-948x.2014.01.018 (in Chinese with English Abstract)
    Di, B. F., Stamatopoulos, C. A., Stamatopoulos, A. C., et al., 2021. Proposal, Application and Partial Validation of a Simplified Expression Evaluating the Stability of Sandy Slopes under Rainfall Conditions. Geomorphology, 395: 107966. https://doi.org/10.1016/j.geomorph.2021.107966
    Dick, G. J., Eberhardt, E., Cabrejo-Liévano, A. G., et al., 2015. Development of an Early-Warning Time-of-Failure Analysis Methodology for Open-Pit Mine Slopes Utilizing Ground-Based Slope Stability Radar Monitoring Data. Canadian Geotechnical Journal, 52(4): 515–529. https://doi.org/10.1139/cgj-2014-0028
    Guo, J., Xu, M., Zhang, Q., et al., 2020. Reservoir Regulation for Control of an Ancient Landslide Reactivated by Water Level Fluctuations in Heishui River, China. Journal of Earth Science, 31(6): 1058–1067. https://doi.org/10.1007/s12583-020-1341-7
    He, K. Q., Wang, S. Q., Du, W., et al., 2008. The Dynamic Parameter of Rainfall: Its Importance in the Prediction of Colluvial Landslides. Bulletin of Engineering Geology and the Environment, 67(3): 345–351. https://doi.org/10.1007/s10064-008-0143-4
    He, K. Q., Wang, S. Q., Du, W., et al., 2010. Dynamic Features and Effects of Rainfall on Landslides in the Three Gorges Reservoir Region, China: Using the Xintan Landslide and the Large Huangya Landslide as the Examples. Environmental Earth Sciences, 59(6): 1267–1274. https://doi.org/10.1007/s12665-009-0114-5
    Huang, C. Z., 2011. Prediction of Landslide Time Under Action of Groundwater. Journal of Engineering Geology, 19(6): 816–822 (in Chinese with English Abstract)
    Huang, F. M., Yin, K. L., Yang, B. B., et al., 2018. Step-Like Displacement Prediction of Landslide Based on Time Series Decomposition and Multivariate Chaotic Model. Earth Science, 43(3): 887–898. https://doi.org/10.3799/dpkx.2018.909 (in Chinese with English Abstract)
    Huang, F. M., Chen, B., Mao, D. X., et al., 2023. Landslide Susceptibility Prediction Modeling and Interpretability Based on Self-Screening Deep Learning Model. Earth Science, 48 (5): 1696–1710. https://doi.org/10.3799/dqkx.2022.247 (in Chinese with English Abstract)
    Iqbal, J., Dai, F. C., Hong, M., et al., 2018. Failure Mechanism and Stability Analysis of an Active Landslide in the Xiangjiaba Reservoir Area, Southwest China. Journal of Earth Science, 29(3): 646–661. https://doi.org/10.1007/s12583-017-0753-5
    Liao, K., Wu, Y. P., Miao, F. S., et al., 2020. Using a Kernel Extreme Learning Machine with Grey Wolf Optimization to Predict the Displacement of Step-Like Landslide. Bulletin of Engineering Geology and the Environment, 79(2): 673–685. https://doi.org/10.1007/s10064-019-01598-9
    Li, Q. Q., Huang, D., Pei, S. F., et al., 2021. Using Physical Model Experiments for Hazards Assessment of Rainfall-Induced Debris Landslides. Journal of Earth Science, 32(5): 1113–1128. https://doi.org/10.1007/s12583-020-1398-3
    Matsuura, S., 2000. Fluctuations of Pore-Water Pressure in a Landslide of Heavy Snow Districts. Landslides, 37(2): 10–19_1. https://doi.org/10.3313/jls1964.37.2_10
    Miao, S. J., Hao, X., Guo, X. L., et al., 2017. Displacement and Landslide Forecast Based on an Improved Version of Saito's Method Together with the Verhulst-Grey Model. Arabian Journal of Geosciences, 10(3): 53. https://doi.org/10.1007/s12517-017-2838-y
    Park, J. Y., Lee, S. R., Lee, D. H., et al., 2019. A Regional-Scale Landslide Early Warning Methodology Applying Statistical and Physically Based Approaches in Sequence. Engineering Geology, 260: 105193. https://doi.org/10.1016/j.enggeo.2019.105193
    Rose, N. D., Hungr, O., 2007. Forecasting Potential Rock Slope Failure in Open Pit Mines Using the Inverse-Velocity Method. International Journal of Rock Mechanics and Mining Sciences, 44(2): 308–320. https://doi.org/10.1016/j.ijrmms.2006.07.014
    Su, A. J., Feng, M. Q., Dong, S., et al., 2022. Improved Statically Solvable Slice Method for Slope Stability Analysis. Journal of Earth Science, 33(5): 1190–1203. https://doi.org/10.1007/s12583-022-1631-3
    Sun, D. L., Xu, J. H., Wen, H. J., et al., 2020. An Optimized Random Forest Model and Its Generalization Ability in Landslide Susceptibility Mapping: Application in Two Areas of Three Gorges Reservoir, China. Journal of Earth Science, 31(6): 1068–1086. https://doi.org/10.1007/s12583-020-1072-9
    Take, W. A., Bolton, M. D., Wong, P. C. P., et al., 2004. Evaluation of Landslide Triggering Mechanisms in Model Fill Slopes. Landslides, 1(3): 173–184. https://doi.org/10.1007/s10346-004-0025-1
    Tang, C. L., 2012. Mechanism Analysis about Load-Unload Response Ratio Theory in Prediction of Landslide. Chinese Journal of Underground Space and Engineering, 8(3): 645–651. https://doi.org/10.3969/j.issn.1673-0836.2012.03.033 (in Chinese with English Abstract)
    Wang, J., Nie, G. G., Xue, C. H., 2020. Landslide Displacement Prediction Based on Time Series Analysis and Data Assimilation with Hydrological Factors. Arabian Journal of Geosciences, 13(12): 460. https://doi.org/10.1007/s12517-020-05452-1
    Wang, L. N., Yan, E. C., Lu, W. B., et al., 2016. Load-Unload Reponse of Colluvial Landslides with Reservoir Water Level Fluctuation and Stability Prediction. Journal of Engineering Geology, 24(6): 1048–1055. https://doi.org/10.13544/j.cnki.jeg.2016.06.002 (in Chinese with English Abstract)
    Xu, X. H., Shang, Y. Q., Wang, Y. C., 2011. Comprehensive Treatment and Evaluation Decision Method of Gravel Soil Landslide. Journal of Jilin University (Earth Science Edition), 41(2): 484–492. https://doi.org/10.13278/j.cnki.jjuese.2011.02.009 (in Chinese with English Abstract)
    Yang, Z. Y., Pourghasemi, H. R., Lee, Y. H., 2016. Fractal Analysis of Rainfall-Induced Landslide and Debris Flow Spread Distribution in the Chenyulan Creek Basin, Taiwan. Journal of Earth Science, 27(1): 151–159. https://doi.org/10.1007/s12583-016-0633-4
    Zaki, A., Chai, H. K., Razak, H. A., et al., 2014. Monitoring and Evaluating the Stability of Soil Slopes: A Review on Various Available Methods and Feasibility of Acoustic Emission Technique. Comptes Rendus Géoscience, 346(9/10): 223–232. https://doi.org/10.1016/j.crte.2014.01.003
    Zhang, W. J., Chen, Y. M., Zhan, L. T., 2006. Loading/Unloading Response Ratio Theory Applied in Predicting Deep-Seated Landslides Triggering. Engineering Geology, 82(4): 234–240. https://doi.org/10.1016/j.enggeo.2005.11.005
    Zeng, P., Wang, Y, H., Zhang, T. L., et al., 2023. Parameter Back Analysis and Stability Prediction of Loess Landslide Based on NSGA-Ⅱ Genetic Algorithm. Earth Science, 48 (5): 1675–1685. https://doi.org/10.3799/dqkx.2023.034 (in Chinese with English Abstract)
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(14)  / Tables(4)

    Article Metrics

    Article views(203) PDF downloads(121) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return