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Probabilistic analysis of geosynthetic-reinforced and pile-supported embankments
KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Soil and Rock Mechanics. School of Energy, Geoscience, Infrastructure and Society, Heriot-Watt University, Edinburgh, EH14 4AS, United Kingdom; Department of Civil Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan.ORCID iD: 0000-0002-9937-3442
2022 (English)In: Computers and geotechnics, ISSN 0266-352X, E-ISSN 1873-7633, Vol. 142, p. 104595-104595, article id 104595Article in journal (Refereed) Published
Abstract [en]

This paper presents a probabilistic analysis of a geosynthetics-reinforced and pile-supported (GRPS) embankment by using reliability, sensitivity and uncertainty propagation techniques. Compared to the traditional deterministic evaluation methods, the analyses performed in this work allow designers to rationally consider the uncertainties of material properties and quantify their influences on the embankment performance. Additionally, complementary information/results could be obtained which enables an informed design decision for engineers. This feature is further enhanced in the paper by proposing a new analysis procedure in which four carefully selected probabilistic techniques are combined with an efficient deterministic model. The contribution of the proposed procedure lies in providing a variety of valuable results (e.g., failure probability and sensitivity index) related to the GRPS embankment performance but with a limited computational time. An application of the procedure is presented in the paper. The obtained results revealed that the studied GRPS embankments have relatively low failure probabilities considering a usual traffic loading but could be risky under extreme loading conditions. Based on the sensitivity analysis, all the random parameters are ranked according to their quantified importance. Some discussions are also provided, trying to link the procedure with practical designs and to explain the uncertainty modeling with more details.

Place, publisher, year, edition, pages
Elsevier BV , 2022. Vol. 142, p. 104595-104595, article id 104595
National Category
Civil Engineering
Research subject
Applied and Computational Mathematics
Identifiers
URN: urn:nbn:se:kth:diva-349231DOI: 10.1016/j.compgeo.2021.104595ISI: 000740752100006Scopus ID: 2-s2.0-85121219235OAI: oai:DiVA.org:kth-349231DiVA, id: diva2:1880129
Note

QC 20240701

Available from: 2024-06-30 Created: 2024-06-30 Last updated: 2024-07-23Bibliographically approved

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Pham, Tuan A.

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