Reliability-Based Stability Analysis of Rock Slopes Using Numerical Analysis and Response Surface Method
Autor: | H. S. Duzgun, N. Dadashzadeh, N. Yesiloglu-Gultekin |
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Rok vydání: | 2017 |
Předmět: |
Computer simulation
business.industry Numerical analysis Monte Carlo method 0211 other engineering and technologies Probabilistic logic Geology 02 engineering and technology Structural engineering 010502 geochemistry & geophysics Geotechnical Engineering and Engineering Geology 01 natural sciences Stability (probability) First-order reliability method Slope stability business Slope stability analysis Algorithm 021101 geological & geomatics engineering 0105 earth and related environmental sciences Civil and Structural Engineering |
Zdroj: | Rock Mechanics and Rock Engineering. 50:2119-2133 |
ISSN: | 1434-453X 0723-2632 |
DOI: | 10.1007/s00603-017-1206-2 |
Popis: | While advanced numerical techniques in slope stability analysis are successfully used in deterministic studies, they have so far found limited use in probabilistic analyses due to their high computation cost. The first-order reliability method (FORM) is one of the most efficient probabilistic techniques to perform probabilistic stability analysis by considering the associated uncertainties in the analysis parameters. However, it is not possible to directly use FORM in numerical slope stability evaluations as it requires definition of a limit state performance function. In this study, an integrated methodology for probabilistic numerical modeling of rock slope stability is proposed. The methodology is based on response surface method, where FORM is used to develop an explicit performance function from the results of numerical simulations. The implementation of the proposed methodology is performed by considering a large potential rock wedge in Sumela Monastery, Turkey. The accuracy of the developed performance function to truly represent the limit state surface is evaluated by monitoring the slope behavior. The calculated probability of failure is compared with Monte Carlo simulation (MCS) method. The proposed methodology is found to be 72% more efficient than MCS, while the accuracy is decreased with an error of 24%. |
Databáze: | OpenAIRE |
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