Engineering Geology for Society and Territory – Volume 2Landslide Processes

Giorgio LollinoDaniele GiordanGiovanni Battista CrostaJordi CorominasRafig AzzamJanusz WasowskiNicola Sciarra Editors

Giorgio Lollino • Daniele GiordanGiovanni Battista CrostaJordi Corominas • Rafig AzzamJanusz Wasowski • Nicola SciarraEditors

Engineering Geologyfor Society and Territory –Volume 2

Landslide Processes

123

EditorsGiorgio LollinoDaniele GiordanInstitute for Geo-hydrological ProtectionNational Research Council (CNR)TurinItaly

Giovanni Battista CrostaDepartment of Earth and Environmental ScienceUniversity of Milan BicoccaMilanItaly

Jordi CorominasDepartment of Geotechnical Engineeringand Geosciences

Universitat Politècnica de CatalunyaBarcelonaSpain

Rafig AzzamDepartment of Engineering Geologyand Hydrogeology

RWTH Aachen UniversityAachenGermany

Janusz WasowskiInstitute for Geo-hydrological ProtectionNational Research Council (CNR)BariItaly

Nicola SciarraUniversity G. D’Annunzi Chieti PescaraChietiItaly

ISBN 978-3-319-09056-6 ISBN 978-3-319-09057-3 (eBook)DOI 10.1007/978-3-319-09057-3Springer Cham Heidelberg New York Dordrecht London

Library of Congress Control Number: 2014946956

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Cover illustration: Landslide affecting the hill of Ruinas, Oristano, Italy. On February, 2005, the phenomenoninvaded the road path and caused difficulties to the traffic. During the emergency, to ensure safety of the road traffic,a particular monitoring and early warning system was deployed. Photo: Daniele Giordan.

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232 GPS Monitoring of the Scopello (Sicily, Italy) DGSD Phenomenon:Relationships Between Surficial and Deep-Seated Morphodynamics . . . . . 1321Valerio Agnesi, Edoardo Rotigliano, Umberto Tammaro, Chiara Cappadonia,Christian Conoscenti, Francesco Obrizzo, Cipriano Di Maggio,Dario Luzio, and Folco Pingue

233 Ganderberg Landslide Characterization Through Monitoring . . . . . . . . . 1327Giulia Bossi, Simone Frigerio, Matteo Mantovani, Gianluca Marcato,Luca Schenato, and Alessandro Pasuto

234 Real-Time Monitoring of Deep-Seated Gravitational SlopeDeformation in the Taiwan Mountain Belt . . . . . . . . . . . . . . . . . . . . . . . 1333Rou-Fei Chen, Ya-Ju Hsu, Shui-Beih Yu, Kuo-Jen Chang, Ruo-Ying Wu,Yu-Chung Hsieh, and Ching-Wee Lin

235 Long-Term Continuous Monitoring of a Deep-Seated CompoundRock Slide in the Northern Apennines (Italy) . . . . . . . . . . . . . . . . . . . . . 1337Alessandro Corsini, Francesco Bonacini, Marco Mulas, Marcello Petitta,Francesco Ronchetti, and Giovanni Truffelli

Part XX Passive Seismic Methods for Unstable Masses Monitoring

236 Use Accelerogram of Real Earthquakes in the Evaluation of theStress-Strain State of Landslide Slopes in Seismically Active Regionsof Ukraine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1343O. Trofymchuk, I. Kaliukh, K. Silchenko, V. Polevetskiy, V. Berchun,and T. Kalyukh

237 Reconstruction of the Geotechnical Model Considering RandomParameters Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1347Calista Monia, Pasculli Antonio and Sciarra Nicola

238 Complex Dynamics of Landslides with Time Delay Under ExternalSeismic Triggering Effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1353Srđan Kostić, Nebojša Vasović, Dragutin Jevremović, Duško Sunarić,Igor Franović, and Kristina Todorović

239 Artificial Neural Networks and Kriging Method for SlopeGeomechanical Characterization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1357Romina Secci, M. Laura Foddis, Alessandro Mazzella, Augusto Montisci,and Gabriele Uras

240 Comparative Study of System Reliability Analysis Methods for SoilSlope Stability. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1363Xiao Liu, D.V. Griffiths, and Hui-ming Tang

241 Simplification of the Stratigraphic Profile in Geotechnical Models ofLandslides: An Analysis Through a Stochastic Approach . . . . . . . . . . . . 1367G. Bossi, L. Borgatti, G. Marcato, and G. Gottardi

xxxii Contents

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240Comparative Study of System ReliabilityAnalysis Methods for Soil Slope StabilityXiao Liu, D.V. Griffiths, and Hui-ming Tang

AbstractGenerally, a soil slope might have numerous potential slip surfaces, and each of them can leadto a failure of the slope. So, the soil slope’s reliability analysis is naturally a system reliabilityproblem, and the overall failure probability may be larger than that of any single slip surface.This paper focuses on the comparative study of system reliability methods for soil slopes.Initially, a rigorous analytic solution is presented, followed by the classification of the existinganalysis methods into four categories. Then, a comparison study between these categories iscarried out. We conclude that the Monte Carlo-based analysis is best suited to resolve thesystem reliability assessment of soil slopes and that within this category the finite-element-based approach is superior to the limit-equilibrium-based one. Other methodologies tend tounderestimate the failure probability of the soil slope system.

KeywordsSlope stability � System reliability �Multiple failure modes � Probabilistic methods �MonteCarlo simulation

240.1 Introduction

A slope reliability analysis often reveals a huge number ofpossible slip surfaces, which combine into an almost infinitenumber of failure models. A well designed reliability anal-ysis should recognize that slope failure analysis is a systemreliability problem and must reveal an overall slope failureprobability. Any reliability analysis that does not confrontthis reality is weakened by simplification.

Cornell (1971) was the first to point out that the overallslope failure probability may be larger than that of any singleslip surface, and this fact has been successively confirmed bymany researchers (e.g., Oka and Wu 1990; Chowdhury andXu 1995; Huang et al. 2010; Zhang et al. 2013). However, aconsiderable number of existing studies (e.g., Hassan andWolff 1999) still focus on the failure probability along asingle slip surface (e.g., the slip surface with maximumfailure probability labeled the probability critical slip sur-face) instead of overall failure probability that involvesmultiple failure modes.

Because the system reliability analysis of slopes is gen-erally more difficult to achieve than calculation of a singlemode, researchers seek and use methods that can simplifythe task. Unfortunately, most of these methodologies tend tounderestimate the failure probability of the system which cancause serious consequences in engineering practice. Soefficacy evaluation of the different methods is essential, andit is critical to recognize their inherent error characteristics.For decades there has been little research aimed at exploringthis fundamental topic.

X. Liu (&) � H.-m. TangThree Gorges Research Center for Geo-hazards of Ministry ofEducation, China University of Geosciences, Wuhan, 430074,Chinae-mail: liuxiao@china.com

X. Liu � D.V. GriffithsDepartment of Civil and Environmental Engineering, ColoradoSchool of Mines, Golden, CO 80401, USA

D.V. GriffithsAustralian Research Council Centre of Excellence forGeotechnical Science and Engineering, University of Newcastle,Callaghan, NSW 2308, Australia

G. Lollino et al. (eds.), Engineering Geology for Society and Territory – Volume 2,DOI: 10.1007/978-3-319-09057-3_240, © Springer International Publishing Switzerland 2015

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number of slip surfaces involved in the slope system is notlimited within the Monte Carlo framework. Increasing thenumber of slip surfaces does not increase the difficulty of thealgorithm programing and if the FEM is applied, it can dealwith the infinite series system of slopes. Second, one nolonger needs to evaluate separately the complex associationbetween each slip surface because the Monte Carlo simu-lation automatically satisfies this requirement. The problemof system reliability is unresolvable by any current analyticsolution because a soil slope can have infinite potential slipsurfaces and the huge number of complex associationsbetween the slip surfaces produce a type of “dimensiondisaster”. In contrast, the third category, which can evaluatean unlimited number of slip surfaces and automaticallyevaluates the associations between each slip surface, isespecially suitable to the system reliability analysis of soilslopes. Moreover, except for current limitations of compu-tational efficiency, FEM is superior to LEM, so improvementin the efficiency of the FEM algorithms is a promisingdirection of future development. In this field, Huang et al.(2010) have suggested an efficient method based on tech-nology of response surface.

240.5 Conclusion

In the complex reality of natural conditions, a slope reli-ability analysis must be considered a system reliabilityproblem. The existing methodologies of system reliabilityanalysis can be divided into four categories, and three ofthem can approach the infinite series system of soil slopes.Theoretical studies have shown that the third category, basedon Monte Carlo simulation, has a much more solid theo-retical foundation than the other categories. If the samesystem is evaluated by all three of these categories ofmethods, the third category will yield the largest failureprobability and will be much closer to the true solution,followed by the second category, with the first yieldingthe smallest one. Moreover, in the third category, theFEM-based approach is superior to the LEM-based one.

Acknowledgements The research work presented here and the prep-aration of this paper have been financially supported by the NationalNature Foundation of China (NSFC; Grant No. 41102195 and No.41230637), the National Basic Research Program of China (973 Pro-gram; Grant No. 2011CB710606), China Postdoctoral Science Foun-dation (Grant No. 2012M521500) and the Special Fund for BasicScientific Research of Central Colleges, China University of Geosci-ences (Wuhan) (Grant No. CUGL100234). All support is gratefullyacknowledged.

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