Abstract
This paper presents a genetic algorithm (GA) to solve the multimodal optimisation problem resulting from 3D slopes prone to multiple regions of failure. A probabilistic approach is taken by using the first-order reliability method (FORM) to approximate the probability of failure. The 3D Bishop method is selected but can be replaced as appropriate. Since 3D analyses have higher computational costs than 2D simulations, we demonstrate that the FORM approach is very practical to large-scale geotechnical problems compared to alternatives such as Monte Carlo simulations (MCS). Furthermore, we show that the GA optimiser can obtain reliability indices and find critical failure regions that would not be found by the MCS easily. These characteristics are demonstrated by some simple test cases and the more complex topography of the Mount St. Helens in the USA.
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References
Adeli H, Cheng N (1993) Integrated genetic algorithm for optimization of space structures. J Aerosp Eng 6(4):315–328
Arai K, Tagyo K (1985) Determination of noncircular slip surface giving the minimum factor fo safety in slopes stability analysis. Soils Found 25:43–51
Bishop A (1955) The use of the slip circle in the stability analysis of earth slopes. Geotechnique 5(1):7–17
Bistacchi A, Massironi M, Superchi L, Zorzi L, Francese R, Chistolini F, Genevois R (2013) A 3D geological model of the 1963 vajont landslide. Ital J Eng Geol Environ 6:531–539
Borja RI, White JA (2010) Continuum deformation and stability analyses of a steep hillside slope under rainfall infiltration. Acta Geotech 5:1–14
Borja RI, White JA, Liu X, Wu W (2012) Factor of safety in a partially saturated slope inferred from hydro-mechanical continuum modeling. Int J Numer Anal Methods Geomech 36(2):236–248
Carrion M, Vargas EA, Velloso RQ, Farfan AD (2017) Slope stability analysis in 3D using numerical limit analysis (NLA) and elasto-plastic analysis (EPA). Geomech Geoeng 12(4):250–265
Cavazzi S, Corstanje R, Mayr T, Hennam J, Fealy R (2013) Are fine resolution digital elevation models always the best choice in digital soil mapping? Geoderma 195–196:111–121
Chen Z (1992) Random trials used in determining global minimum factors of safety of slopes. Can Geotech J 29:225–233
Chen Z, Mi H, Zhang F, Wang X (2003) A simplified method for 3D slope stability analysis. Can Geotech J 40:675–683
Chen HX, Zhang S, Peng M, Zhang LM (2016) A physically-based multi-hazard risk assessment platform for regional rainfall-induced slope failures and debris flows. Eng Geol 203:15–29
Coello CC, Lamont BG, van Veldhuizen AD (2007) Evolutionary algorithms for solving multi-objective problems. Springer, Berlin
Cui L, Sheng D (2005) Genetic algorithms in probabilistic finite element analysis of geotechnical problems. Comput Geotech 32:555–563
Deb K (2001) Multi-objective optimisation using evolutionary algorithms. Wiley, Hoboken
Donald I, Giam P (1995) The ACADS slope stability review. In: 6th international symposium on Lanslides, Christchurch
Donnadieu F, Merle O, Besson J-C (2001) Volcanic edifice stability during cryptodome intrusion. Bull Volcanol 63:61–72
Gao W (2005) Method for searching critical slip surface of soil slope base on ant colony algorithm. J Hydraul Eng 36(9):1100–1104 (in Chinese)
Gitirana G, Santos MA, Fredlund M (2008) Three-dimensional analysis of the Lodalen Landslide. In: GeoCongress, New Orleans, LA. https://doi.org/10.1061/40971(310)23
Goldberg D (1989) Genetic algorithms in search, optimisational and machine learning. Addison-Wesley, Boston
Greco V (1996) Efficient Monte Carlo technique for locating critical slip surface. J Geotechn Eng 122(7):517–525
Hajela P (1990) Genetic search: an approach to the non-convex optimisation problem. AIAA J 28(7):1205–1210
Holland HJ (1975) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT Press, Cambridge
Hungr O (1987) An extension of Bishop’s simplified method of slope stability analysis to three dimensions. Geotechnique 37(1):113–117
Hungr O, Salgado F, Bryne P (1989) Evaluation of a three-dimensional method of slope stability analysis. Can Geotech J 26(4):679–686
Jenkins W (1991) Towards structural optimisation via genetic algorithms. Comput Struct 40(5):1321–1327
Kahatadeniya K (2009) Determination of the critical failure surface for slope stability analysis using ant colony optimisation. Eng Geol 108:133–141
Kaymaz I (2005) Application of kriging method to structural reliability problems. Struct Saf 2005:133–151
Liang JJ, Qin AK (2006) Comprehensive learning particle swarm optimiser for global optimisation of multimodal functions. IEEE Trans Evol Comput 10(3):281–295
Llano-Serna MA, Farias MM, Pedroso DM (2015) An assessment of the material point method for modelling large scale run-out processes in landslides. Landslides 13:1057–1066
McCombie P, Wilkinson P (2002) The use of the simple genetic algorithm in finding the critical factor of safety in slope stability analysis. Comput Geotech 29:699–714
Michalewicz Z (1996) Genetic algorithms data structures evolution programs. Springer, Springer
Monde G (2016) Create contours and DEM using Google Earth and QGIS 2.10. Monde Geospatial. https://www.youtube.com/watch?v=bLbY3iMBW-A. Accessed 5 March 2016
Nikolakopoulos KG, Kamaratakis EK, Chrysoulakis N (2006) SRTM vs ASTER elevation products. Comparison for two regions in Crete, Greece. Int J Remote Sens 27(21):4819–4838
Pal S, Wathugala G, Kundu S (1996) Calibration of a constitutive model using genetic algorithms. Comput Geotech 19(4):325–348
Pedroso DM, Williams DJ (2011) Automatic calibration of soil–water characteristic curves using genetic algorithms. Comput Geotech 38:330–340
Pham H, Fredlund D (2003) The application of dynamic programming to slope stability analysis. Comput Geotech 40(4):830–847
Reale C, Xue J, Pan Z, Gavin K (2015) Deterministic and probabilistic multip-modal analysis of slope stability. Comput Geotech 66:172–179
Reid M, Christian S, Brien D (2000) Gravitational stability of three-dimensional stratovolcano edifices. J Geophys Res 105:6043–6056
Reid ME, Christian SB, Brien DL, Henderson ST (2015) USGS science for a changing world. https://pubs.er.usgs.gov/publication/tm14A1. Accessed Nov 2015
Reid ME, Christian SB, Brien DL (2015) Scoops3D: software to analyze 3D slope stability throughout a digital landscape. USGS. http://landslides.usgs.gov/research/software.php. Accessed 14 Nov 2015
Rezaeean A, Noorzad R, Dankoub AKM (2011) Ant colony optimisation for locating the critical failure surface in slope stability analysis. World Appl Sci J 13(7):1702–1711
Simpson A, Priest S (1993) The application of genetic algorithms to optimization problems in geotechnics. Comput Geotech. 15(1):1–19
Sulebak JR (2000) Applications of digital elevation models. In: SINTEF
Tran C, Srokosz P (2010) The idea of PGA stream computations for soil slope stability evaluation. CR Mec 338:499–509
Tun YW, Pedroso DM, Scheuermann A, Williams DJ (2016) Probabilistic reliability analusis of multiple slopes with genetic algorithms. Comput Geotech 77:68–76
Wang Y (2011) Practical reliability analysis of slope stability by advanced Monte Carlo simulations in a spreadsheet. Can Geotech J 48:162–172
Xie M, Esaki T, Cai M (2004) A GIS-based method for locating the critical 3D slip surface in a slope. Comput Geotech 31:267–277
Zhang H, Dai H, Beer M, Wang W (2013) Structural reliability analysis on the basis of small samples: an interval quasi-Monte Carlo method. Mech Syst Signal Process 37:137–151
Zolfaghari AR, Heath AC, McCombie PF (2005) Simple genetic algorithm search for critical non-circular failure surface in slope stability analysis. Comput Geotech 32:139–152
Acknowledgements
The support from the Australian Research Council under Grant DE120100163 is gratefully acknowledged. We also thank the developers of the free software Scoops3D and QGIS and the team behind the Google Earth software.
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Tun, Y.W., Llano-Serna, M.A., Pedroso, D.M. et al. Multimodal reliability analysis of 3D slopes with a genetic algorithm. Acta Geotech. 14, 207–223 (2019). https://doi.org/10.1007/s11440-018-0642-9
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DOI: https://doi.org/10.1007/s11440-018-0642-9