Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.compstruc.2011.02.016
DC FieldValue
dc.titleEffects of soil spatial variability on rainfall-induced landslides
dc.contributor.authorSantoso, A.M.
dc.contributor.authorPhoon, K.-K.
dc.contributor.authorQuek, S.-T.
dc.date.accessioned2014-10-09T07:40:13Z
dc.date.available2014-10-09T07:40:13Z
dc.date.issued2011-06
dc.identifier.citationSantoso, A.M., Phoon, K.-K., Quek, S.-T. (2011-06). Effects of soil spatial variability on rainfall-induced landslides. Computers and Structures 89 (11-12) : 893-900. ScholarBank@NUS Repository. https://doi.org/10.1016/j.compstruc.2011.02.016
dc.identifier.issn00457949
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/91288
dc.description.abstractThis paper presents a probabilistic framework to assess the stability of unsaturated slope under rainfall. The effects of soil spatial variability on the probability of rainfall-induced slope failure (landslides) are investigated. Soil spatial variability is considered by modeling the saturated hydraulic conductivity of the soil (ks) as a stationary lognormal random field. Subset simulation with a modified Metropolis-Hastings algorithm is used to estimate the probability of slope failure. It is demonstrated numerically that probabilistic analysis accounting for spatial variability of ks can reproduce a shallow failure mechanism widely observed in real rainfall-induced landslides. This shallow failure is attributed to positive pore-water pressures developed in layers near the ground surface. In contrast, analysis assuming a homogeneous profile cannot reproduce a shallow failure except for the extreme case of infiltration flux being almost equal to ks. Therefore, ignoring spatial variability leads to unconservative estimates of failure probability. The correlation length of ks affects the probability of slope failure significantly. The applicability of subset simulation with a modified Metropolis-Hastings algorithm to assess the reliability of problems involving spatial variability is highlighted. © 2010 Elsevier Ltd. All rights reserved.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/j.compstruc.2011.02.016
dc.sourceScopus
dc.subjectMetropolis-Hastings algorithm
dc.subjectProbabilistic analysis
dc.subjectRainfall-induced landslide
dc.subjectSpatial variability
dc.subjectSubset simulation
dc.subjectUnsaturated seepage
dc.typeConference Paper
dc.contributor.departmentCIVIL & ENVIRONMENTAL ENGINEERING
dc.description.doi10.1016/j.compstruc.2011.02.016
dc.description.sourcetitleComputers and Structures
dc.description.volume89
dc.description.issue11-12
dc.description.page893-900
dc.description.codenCMSTC
dc.identifier.isiut000292418000007
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