Abstract
Groundwater lowering is one of the most important countermeasures to avoid the risk of rainfall-triggered landslides. However, the long-term reliability of many drainage methods is often a matter of concern since the drains may easily get clogged. A new hydraulic-driven self-starting drainage method is presented in this paper. In the proposed Random Forest (RF) based robust design approach for the self-starting drains, the datasets are generated using an automatically controlled numerical modeling technology. The deterministic analysis is carried out based on uncertain soil parameters and the specific designs selected using Uniform Design (UD). The ensemble of RF models is applied in the design process to improve computing efficiency. Safety requirements, design robustness, and cost efficiency are simultaneously considered utilizing multi-objective optimization. A straightforward and efficient framework that focuses on difficulties caused by an enormous design space is established for the robust design of the self-starting drains, and improved computation efficiency is achieved. The effectiveness of the proposed approach is illustrated with a case study, the Qili landslide in Zhejiang Province, China.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Alcántara-Ayala I, Moreno AR (2016) Landslide risk perception and communication for disaster risk management in mountain areas of developing countries: a Mexican foretaste. J Mt Sci 13(12): 2079–2093. https://doi.org/10.1007/s11629-015-3823-0
Breiman L (2001) Random Forests. Machine Learning 45: 5–32. https://doi.org/10.1023/A:1010933404324
Calvello M, d’Orsi RN, Piciullo L, et al. (2015) The rio de janeiro early warning system for rainfall-induced landslides: analysis of performance for the years 2010–2013. Int J Dis Risk Reduction 12: 3–15. https://doi.org/10.1016/j.ijdrr.2014.10.005
Chen W, Xie XS, Peng JB, et al. (2018) GIS-based landslide susceptibility evaluation using a novel hybrid integration approach of bivariate statistical based random forest method. Catena 164: 135–149. https://doi.org/10.1016/j.catena.2018.01.012
Conte E, Troncone A (2018) A performance-based method for the design of drainage trenches used to stabilize slopes. Eng Geol 239: 158–166. https://doi.org/10.1016/j.enggeo.2018.03.017
Cotecchia F, Lollino P, Petti R (2016) Efficacy of drainage trenches to stabilise deep slow landslides in clay slopes. Géotech Lett 6: 1–6. https://doi.org/10.1680/jgele.15.00065
Deb K, Gupta S (2011) Understanding knee points in bicriteria problems and their implications as preferred solution principles. Eng Optim 43(11): 1175–1204. https://doi.org/10.1080/0305215X.2010.548863
Deb K, Pratap A, Agarwal S, Meyarivan TAMT (2002) A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evol Comput 6(2): 182–197. https://doi.org/10.1109/4235.996017
Dou J, Yunus AP, Bui DT, et al. (2019) Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan. Sci Total Environ 662: 332–346. https://doi.org/10.1016/j.scitotenv.2019.01.221
Fan BQ, Wang LQ, Gong WP, et al. (2020) Improved robust design of rock wedge slopes with a new robustness measure. Comput Geotech 123: 103548. https://doi.org/10.1016/j.compgeo.2020.103548
Fang KT, Lin DKJ (2003) Uniform experimental designs and their applications in industry. In: Khattree R, Rao CR, (Ed.), Handbook of Statistics 22: 131–170. https://doi.org/10.1016/S0169-7161(03)22006-X
Fang KT, Lin DKJ, Winker P, et al. (2000) Uniform design: theory and application. Technometrics 42(3): 237–48. https://doi.org/10.1080/00401706.2000.10486045
Geo-slope international ltd (2012a) Seep/W, Groundwater seepage analysis. Geo-slope international ltd, Calgary, Alberta, Canada.
Geo-slope international ltd (2012b) Slope/W, Slope stability analysis. Geo-slope international ltd, Calgary, Alberta, Canada.
Gong WP, Huang HW, Juang CH et al. (2015) Improved shield tunnel design methodology incorporating design robustness. Can Geotech J 52(10): 150226181849001. https://doi.org/10.1139/cgj-2014-0458
Gong WP, Juang CH, Khoshnevisan S, et al. (2016) R-lrfd: load and resistance factor design considering robustness. Comput Geotech 74: 74–87. https://doi.org/10.1016/j.compgeo.2015.12.017
Gong WP, Khoshnevisan S, Juang CH (2014) Gradient-based design robustness measure for robust geotechnical design. Can Geotech J 51(11): 1331–1342. https://doi.org/10.1139/cgj-2013-0428
Gong WP, Tang, HM, Juang CH, et al. (2020) Optimization design of stabilizing piles in slopes considering spatial variability. Acta Geotech 15: 3243–3259. https://doi.org/10.1007/s11440-020-00960-6
Guo X, Li Y, Cui P, et al. (2016) Discontinuous slope failures and pore-water pressure variation. J Mt Sci. 13: 116–125. https://doi.org/10.1007/s11629-015-3528-4
Huang CM, Lee YJ, Lin DK, et al. (2007) Model selection for support vector machines via uniform design. Comput Stat Data Anal 52(1): 335–346. https://doi.org/10.1016/j.csda.2007.02.013
Juang CH, Wang L, Atamturktu S, et al. (2012) Reliability-based robust and optimal design of shallow foundations in cohesionless soil in the face of uncertainty. J GeoEng 7(3): 75–87. https://doi.org/10.6310/jog.2012.7(3).1
Juang CH, Wang L, Khoshnevisan S, et al. (2013a) Robust geotechnical design: Methodology and applications. J GeoEngin 8(3): 61–70. https://doi.org/10.6310/jog.2013.8(3).1
Juang CH, Wang L (2013b) Reliability-based robust geotechnical design of spread foundations using multi-objective genetic algorithm. Comput Geotech 48: 96–106. https://doi.org/10.1016/j.compgeo.2012.10.003
Khoshnevisan S, Gong W, Wang L, et al. (2014) Robust design in geotechnical engineering-an update. Georisk: Assess Manage Risk Eng Syst Geohazards 8(4): 217–234. https://doi.org/10.1080/17499518.2014.980274
Khoshnevisan S, Gong WP, Juang CH, et al. (2015) Efficient Robust Geotechnical Design of Drilled Shafts in Clay Using a Spreadsheet. J Geotech Geoenviron 141(2): 1–13. https://doi.org/10.1061/(ASCE)GT.1943-5606.0001214
Li X, Li X, Su Y (2016) A hybrid approach combining uniform design and support vector machine to probabilistic tunnel stability assessment. Struct Saf 61: 22–42. https://doi.org/10.1016/j.strusafe.2016.03.001
Lim TT, Rahardjo H, Chang MF, et al. (1996) Effect of rainfall on matric suctions in a residual soil slope. Can Geotech J 33(4): 618–628. https://doi.org/10.1139/t96-087
Liu ZQ, Gilbert G, Cepeda JM, et al. (2020) Modelling of shallow landslides with machine learning algorithms. Geosci Front 4(14). https://doi.org/10.1016/j.gsf.2020.04.014
Lü Q, Chan CL, Low BK (2012) Probabilistic evaluation of ground-support interaction for deep rock excavation using artificial neural network and uniform design. Tunn Undergr Sp Tech 32: 1–18. https://doi.org/10.1016/j.tust.2012.04.014
Lü Q, Xiao ZP, Ji J, et al. (2017) Moving least squares method for reliability assessment of rock tunnel excavation considering ground-support interaction. Comput Geotech 84: 88–100. https://doi.org/10.1016/j.compgeo.2016.11.019
Matti B, Tacher L, Commend S (2012) Modelling the efficiency of a drainage gallery workfor a large landslide with respect tohydrogeological heterogeneity. Can Geotech J 49: 968–985. https://doi.org/10.1139/t2012-061
Mei SH, Sheng Q, Feng XT (2004) Application of uniform design to geotechnical engineering. J Rock Mech and Eng 23(16): 2694–2697. https://doi.org/10.47297/taposatWSP2633-456902.20200103
Moolayil J (2019) Deep Neural Networks for Supervised Learning: Regression. Learn Keras for Deep Neural Networks. Apress, Berkeley, CA, 53–99. https://doi.org/10.1007/978-1-4842-4240-7_3
Mori H, Chen X, Leung YF, et al. (2020) Landslide hazard assessment by smoothed particle hydrodynamics with spatially variable soil properties and statistical rainfall distribution. Can Geotech J. https://doi.org/10.1139/cgj-2019-0601
Mukhlisin M (2016) Study of Horizontal Drain Effect on Slope Stability. J Geol Soc India 87(4): 483–490. https://doi.org/10.1007/s12594-016-0417-6
Peng X, Li DQ, Cao ZJ, et al. (2017) Reliability-based robust geotechnical design using Monte Carlo simulation. Bull Eng Geol Environ 76: 1217–1227. https://doi.org/10.1007/s10064-016-0905-3
Phoon K, Kulhawy FH (1999) Characterization of geotechnical variability. Can Geotech J 36(4): 612–624. https://doi.org/10.1139/t99-038
Sun DL, Wen HJ, Wang DZ, et al. (2020) A random forest model of landslide susceptibility mapping based on hyperparameter optimization using Bayes algorithm. Geomorphology 362: 107201. https://doi.org/10.1016/j.geomorph.2020.107201
Sun HY, Wang DF, Shang YQ. et al. (2019a) An improved siphon drainage method for slope stabilization. J Mt. Sci. 16: 701–713. https://doi.org/10.1007/s11629-018-5171-3
Sun HY, Wong LNY, Shang YQ, et al. (2010) Evaluation of drainage tunnel effectiveness in landslide control. Landslides 7: 445–454. https://doi.org/10.1007/s10346-010-0210-3
Sun HY, Shuai FX, Shang YQ, et al. (2019b) Study on negative pressure drainage method of downdip bolehole in slope. J Eng Geol 27(3): 585–591 (In Chinese). https://doi.org/10.13544/j.cnki.jeg.2018-230
Sun HY, Wu X, Wang DF, et al. (2018) Analysis of deformation mechanism of landslide in complex geological conditions. Bull Eng Geol Environ 78(6) 4311–4323. https://doi.org/10.1007/s10064-018-1406-3
Tan XH, Shen MF, Juang CH, et al. (2020) Modified robust geotechnical design approach based on the sensitivity of reliability indexnt. Probabilist Eng 60: 103049. https://doi.org/10.1016/j.probengmech.2020.103049
Wang Z, Yu Y, Sun HY, et al. (2019) Robust optimization of the constructional time delay in the design of double-row stabilizing piles. Bull Eng Geol Environ 79: 53–67. https://doi.org/10.1007/s10064-019-01554-7
Wei ZL, Shang YQ, Sun HY, et al. (2019) The effectiveness of a drainage tunnel in increasing the rainfall threshold of a deep-seated landslide. Landslides 16: 1731–1744. https://doi.org/10.1007/s10346-019-01241-4
Yu Y, Shen MF, Sun HY, et al. (2019) Robust design of siphon drainage method for stabilizing rainfall-induced landslides. J Eng Geol 249: 186–197. https://doi.org/10.1016/j.enggeo.2019.01.001
Zhang DM, Zhai WZ, Huang HW, et al. (2019) Robust retrofitting design for rehabilitation of segmental tunnel linings: Using the example of steel plates. Tunn Undergr Sp Tech 83: 231–241. https://doi.org/10.1016/j.tust.2018.09.016
Zhang YJ, Shen MF, Juang CH, et al. (2020) Fractile-based method for selecting characteristic values for geotechnical design with LRFD. Soils Found 60(1): 115–128. https://doi.org/10.1016/j.sandf.2020.01.010
Zhong ZL, Zhang SH, Zhao M, et al. (2020) Reliability-based robust geotechnical design of spread foundations considering multiple failure modes. Comput Geotech 119: 103292. https://doi.org/10.1016/j.compgeo.2019.103292
Acknowledgements
This study was supported by the National Natural Science Foundation of China (Grant No. 41772276) and the Key R&D project of Zhejiang Province (Grant No.2017C03006). Thanks are given for the geological survey and design information provided by Zhejiang Provincial Institute of Communications Planning, Design & Research, and the monitoring data provided by Qianchao Construction Co., Ltd. of Zhejiang Communications Construction Group.
The first author wishes to thank the Zhejiang University and the Norwegian Geotechnical Institute for funding his research stay at NGI.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Ge, Q., Liu, Zq., Sun, Hy. et al. Robust design of self-starting drains using Random Forest. J. Mt. Sci. 18, 973–989 (2021). https://doi.org/10.1007/s11629-020-6202-4
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11629-020-6202-4