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Investigation of model uncertainty for block stability analysis
Author(s) -
Bagheri Mehdi,
Stille Håkan
Publication year - 2011
Publication title -
international journal for numerical and analytical methods in geomechanics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.419
H-Index - 91
eISSN - 1096-9853
pISSN - 0363-9061
DOI - 10.1002/nag.926
Subject(s) - stiffness , joint (building) , stress (linguistics) , stability (learning theory) , block (permutation group theory) , probabilistic logic , structural engineering , mathematics , mathematical optimization , engineering , computer science , geometry , statistics , philosophy , linguistics , machine learning
The application of probabilistic design, such as FORM, is expanding rapidly in the design of geotechnical structures. The analytical solution proposed by Crawford and Bray for analyzing block stability can be used as a performance function to carry out probabilistic design. The solution benefits from considering both clamping forces and joint stiffness. However, imperfect assumptions and simplifications in the solution generate model uncertainties. The amount of model uncertainty must be considered in order to assess a reliable design. The purpose of this paper is to identify when the analytical solution is applicable and quantify the model uncertainty of the solution. The amount of model uncertainty for the analytical solution has been assessed for different conditions. The results show that at a shallow depth with a low value of in situ stress ratio (horizontal stress/vertical stress), the analytical solution predicts that the block is stable whereas DEM shows that the block is unstable. The results of the analyses indicate that in cases with low stress ratio, cases with high anisotropy of joint stiffness or the case of a semiapical angle close to the friction angle, the accuracy of the analytical solution is low. Neglecting key parameters, such as the absolute value of joint shear and normal stiffness, vertical in situ stress and its influence on joint relaxation generate model uncertainty in the analytical solution. The analyses show that by having more information about the key parameters, the model uncertainty factor could be identified more precisely. Copyright © 2010 John Wiley & Sons, Ltd.