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A new method for calculating failure probability of landslide based on ANN and a convex set model

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Abstract

Calculating the failure probability of a landslide is important in engineering related to geological process and geomorphological evolution. Strength parameters of a soil (i.e., cohesion and internal friction angle) are regarded as uncertain-but-bounded parameters. In this study, a new method is proposed for computing the landslide failure probability based on a convex set model and artificial neural network (ANN). In the new method, ANN is used to determine the limit state function of landslide stability, and the failure probability is determined using a simple iterative algorithm. The new method was applied to calculate the failure probability of the Gufenping landslide in Nanjiang, Sichuan, China. The results calculated using a Monte Carlo simulation (MCS) method confirmed that the new method accurately and quickly obtains the failure probability of a landslide. Additionally, compared with the two-dimensional calculation method, the one-dimensional analysis method overestimates the failure probability of the landslide. The results of single factor and global sensitivity analysis indicate that the average of internal friction angle is the main factor affecting the stability of the landslide. It is easy to calculate failure probability of landslides using the novel method than using the conventional methods.

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References

  • Ben-Haim Y, Elishakoff I (1990) Convex models of uncertainty in applied mechanics. Elsevier Science Publisher, Amsterdam, pp 10–25

    Google Scholar 

  • Bragagnolo L, da Silva RV, Grzybowski JMV (2020) Artificial neural network ensembles applied to the mapping of landslide susceptibility. Catena 184:104240

    Article  Google Scholar 

  • Budil DE, Lee S, Saxena S, Freed JH (1996) Nonlinear-least-squares analysis of slow-motion EPR spectra in one and two dimensions using a modified Levenberg-Marquardt algorithm. J Magnet Resonanc Ser A 120(2):155–189

    Article  Google Scholar 

  • Cannavo F (2012) Sensitivity analysis for volcanic source modeling quality assessment and model selection. Comput Geosci 44:52–59

    Article  Google Scholar 

  • Cao WG, Zhang YJ (2007) Non-probabilistic fuzzy reliability analysis of slope stability based on interval interconnection method. China Civil Eng J 40(11):64–69

    Google Scholar 

  • Cheng YM, Yip CJ (2007) Three-dimensional asymmetrical slope stability analysis extension of Bishop’s, Janbu’s, and Morgenstern–Price’s techniques. J Geotech Geoenviron 133(12):1544–1555

    Article  Google Scholar 

  • Ching JY, Phoon KK, Hu YG (2009) Efficient evaluation of reliability for slopes with circular slip surfaces using importance sampling. J Geotech Geoenviron 135(6):768–777

    Article  Google Scholar 

  • Cho SE (2009) Probabilistic stability analyses of slopes using the ANN-based response surface. Comput Geotech 36(5):787–797

    Article  Google Scholar 

  • Feng XT, Zhao HB, Li SJ (2004) Modeling non-linear displacement time series of geo-materials using evolutionary support vector machines. Int J Rock Mech Min Sci 41(7):1087–1107

    Article  Google Scholar 

  • Ganzerli S, Pantelides CP (1999) Load and resistance convex models for optimum design. Struct Optimiz 17(4):259–268

    Article  Google Scholar 

  • Gao Y, Sun DA, Zhu ZC, Xu YF (2019) Hydromechanical behavior of unsaturated soil with different initial densities over a wide suction range. Acta Geotech 14(2):417–428

    Article  Google Scholar 

  • Gavin K, Xue J (2009) Use of a genetic algorithm to perform reliability analysis of unsaturated soil slopes. Géotechnique 59(6):545–549

    Article  Google Scholar 

  • Geological Survey Research Institute of Chengdu University of Technology. (2015) Geological hazard survey of Nanjiang county sheet in the bahe basin of east Sichuan (investigation report of Gufenping landslide in Dongyu town, Nanjiang county) (survey report no. 1212011220169).

  • Ghaderi A, Shahri AA, Larsson S (2019) An artificial neural network based model to predict spatial soil type distribution using piezocone penetration test data (CPTu). Bull Eng Geol Environ 78(6):4579–4588

    Article  Google Scholar 

  • Hamby DM (1994) A review of techniques for parameter sensitivity analysis of environmental-models. Environ Monit Assess 32(2):135–154

    Article  Google Scholar 

  • Haque U, da Silva PF, Devoli G, Pilz J, Zhao BX, Khaloua A, Wilopo W, Andersen P, Lu P, Lee J, Yamamoto T, Keellings D, Wu JH, Glass GE (2019) The human cost of global warming: deadly landslides and their triggers (1995-2014). Sci Total Environ 682:673–684

    Article  Google Scholar 

  • Hoang ND, Pham AD (2016) Hybrid artificial intelligence approach based on metaheuristic and machine learning for slope stability assessment: a multinational data analysis. Expert Syst Appl 46:60–68

    Article  Google Scholar 

  • Huang JS, Fenton G, Griffiths DV, Li DQ, Zhou CB (2017) On the efficient estimation of small failure probability in slopes. Landslides 14(2):491–498

    Article  Google Scholar 

  • Huang JS, Zheng D, Li DQ, Kelly R, Sloan SW (2018) Probabilistic characterization of two-dimensional soil profile by integrating cone penetration test (CPT) with multi-channel analysis of surface wave (MASW) data. Can Geotech J 55(8):1168–1181

    Article  Google Scholar 

  • Janbu N (1968) Slope stability computations. Soil mechanics and foundation engineering report. Technical University of Norway, Trondheim

    Google Scholar 

  • Jiang C, Bi RG, Lu GY, Han X (2013) Structural reliability analysis using non-probabilistic convex model. Comput Methods Appl Mech Eng 254:83–98

    Article  Google Scholar 

  • Johari A, Rahmati H (2019) System reliability analysis of slopes based on the method of slices using sequential compounding method. Comput Geotech 114:103116

    Article  Google Scholar 

  • Kang Z, Luo YJ, Li A (2011) On non-probabilistic reliability-based design optimization of structures with uncertain-but-bounded parameters. Struct Saf 33(3):196–205

    Article  Google Scholar 

  • Li AJ, Khoo S, Lyamin AV, Wang Y (2016) Rock slope stability analyses using extreme learning neural network and terminal steepest descent algorithm. Autom Constr 65:42–50

    Article  Google Scholar 

  • Li XY, Zhang LM, Zhang S (2018) Efficient Bayesian networks for slope safety evaluation with large quantity monitoring information. Geosci Front 9(6):1679–1687

    Article  Google Scholar 

  • Li SH, Wu LZ, Chen JJ, Huang RQ (2020) Multiple data-driven approach for predicting landslide deformation. Landslides 17(3):709–718

    Article  Google Scholar 

  • Li SH, Wu LZ, Huang JS (2021) A novel mathematical model for predicting landslide displacement. Soft Comput 25(3):2453–2466

  • Lin F, Wu LZ, Huang RQ, Zhang H (2018) Formation and characteristics of the Xiaoba landslide in Fuquan, Guizhou. China. Landslides 15(4):669–681

    Article  Google Scholar 

  • Liu LL, Cheng YM, Wang XM (2017) Genetic algorithm optimized Taylor Kriging surrogate model for system reliability analysis of soil slopes. Landslides 14(2):535–546

    Article  Google Scholar 

  • Lu N, Godt JW (2011) Hillslope hydrology and stability. Cambridge University Press, Cambridge, pp 126–129

    Google Scholar 

  • Luo YJ, Kang Z, Luo Z, Li A (2009) Continuum topology optimization with non-probabilistic reliability constraints based on multi-ellipsoid convex model. Struct Multidiscip Optim 39(3):297–310

    Article  Google Scholar 

  • Lv P, Wang XL, Liu Z, Yu J, Liu MH (2017) Porosity- and reliability-based evaluation of concrete-face rock dam compaction quality. Autom Constr 81:196–209

    Article  Google Scholar 

  • Morgenstern NR, Price VE (1965) The analysis of the stability of general slip surfaces. Geotechnique 15:79–93

    Article  Google Scholar 

  • Ozcag E, Ege I, Gürçay H, Jolevska-Tuneska B (2008) On partial derivatives of the incomplete beta function. Appl Math Lett 21(7):675–681

    Article  Google Scholar 

  • Reale C, Xue JF, Pan ZM, Gavin K (2015) Deterministic and probabilistic multi-modal analysis of slope stability. Comput Geotech 66:172–179

    Article  Google Scholar 

  • Rubio E, Hall JW, Anderson MG (2004) Uncertainty analysis in a slope hydrology and stability model using probabilistic and imprecise information. Comput Geotech 31(7):529–536

    Article  Google Scholar 

  • Saltelli A (2002) Sensitivity analysis for importance assessment. Risk Anal 22(3):579–590

    Article  Google Scholar 

  • Sarkar S, Roy AK, Raha P (2016) Deterministic approach for susceptibility assessment of shallow debris slide in the Darjeeling Himalayas, India. Catena 142:36–46

    Article  Google Scholar 

  • Sassa K, Fukuoka H, Wang FW, Wang GH (2005) Landslides: risk analysis and sustainable disaster management. Springer-Verlag, Berlin Heidelberg

    Book  Google Scholar 

  • Shahri AA (2016) An optimized artificial neural network structure to predict clay sensitivity in a high landslide prone area using piezocone penetration test (CPTU) data: a case study in southwest of Sweden. Geotech Geol Eng 34(2):745–758

    Article  Google Scholar 

  • Shahri AA, Spross J, Johansson F, Larsson S (2019) Landslide susceptibility hazard map in southwest Sweden using artificial neural network. Catena 183:104225

    Article  Google Scholar 

  • Singh J, Banka H, Verma AK (2019) A BBO-based algorithm for slope stability analysis by locating critical failure surface. Neural Comput & Applic 31(10):6401–6418

    Article  Google Scholar 

  • Srđan K, Nebojša V, Duško S (2015) A new approach to grid search method in slope stability analysis using Box–Behnken statistical design. Appl Math Comput 256(1):425–437

    Google Scholar 

  • Tang Q, Hu XB (2020) Modeling individual travel time with back propagation neural network approach for advanced traveler information systems. J Transport Eng Part A Syst 146(6):04020039

    Article  Google Scholar 

  • Wang J, Qiu ZP (2010) The reliability analysis of probabilistic and interval hybrid structural system. Appl Math Model 34(11):3648–3658

    Article  Google Scholar 

  • Werner G (1975) Linear algebra. Springer, New York, pp 10–33

    Google Scholar 

  • Wu LZ, Zhou Y, Sun P, Shi JS, Liu GG, Bai LY (2017) Laboratory characterization of rainfall-induced loess slope failure. Catena 150:1–8

    Article  Google Scholar 

  • Wu LZ, Huang JS, Fan W, Li X (2020) Hydro-mechanical coupling in unsaturated soils covering a non-deformable structure. Comput Geotech 117:103287

    Article  Google Scholar 

  • Yang ZY, Ching JY (2019) A novel simplified geotechnical reliability analysis method. Appl Math Model 74:337–349

    Article  Google Scholar 

  • Yang BB, Yin KL, Xiao T, Chen LX, Du J (2017) Annual variation of landslide stability under the effect of water level fluctuation and rainfall in the Three Gorges Reservoir, China. Environ Earth Sci 76(16):564

    Article  Google Scholar 

  • Zain AM, Haron H, Sharif S (2010) Prediction of surface roughness in the end milling machining using artificial neural network. Expert Syst Appl 37(2):1755–1768

    Article  Google Scholar 

  • Zhang WG, Goh ATC (2018) Reliability analysis of geotechnical infrastructures: introduction. Geosci Front 9(6):1595–1596

    Article  Google Scholar 

  • Zhang J, Huang HW, Zhang LM, Zhu HH, Shi B (2014) Probabilistic prediction of rainfall-induced slope failure using a mechanics-based model. Eng Geol 168(16):129–140

    Article  Google Scholar 

  • Zhou J, Li EM, Yang S, Wang MZ, Shi XZ, Yao S, Mitri HS (2019) Slope stability prediction for circular mode failure using gradient boosting machine approach based on an updated database of case histories. Saf Sci 118:505–518

    Article  Google Scholar 

Download references

Acknowledgements

We thank the National Key Research and Development Program of China (no. 2018YFC1504702) and the National Natural Science Foundation of China (no. 41790432).

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Authors and Affiliations

  1. Department of Geological Engineering, Southwest Jiaotong University, Chengdu, 610031, Sichuan, China

    S. H. Li

  2. State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, 610059, Sichuan, China

    S. H. Li & X. H. Luo

  3. College of Civil Engineering and Geomatics, Southwest Petroleum University, Sichuan, Chengdu, China

    L. Z. Wu

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  1. S. H. Li
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  2. X. H. Luo
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  3. L. Z. Wu
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Correspondence to L. Z. Wu.

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Li, S.H., Luo, X.H. & Wu, L.Z. A new method for calculating failure probability of landslide based on ANN and a convex set model. Landslides 18, 2855–2867 (2021). https://doi.org/10.1007/s10346-021-01652-2

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