期刊
COMPUTERS AND GEOTECHNICS
卷 43, 期 -, 页码 26-36出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.compgeo.2012.02.002
关键词
Uncertainty; Bayesian method; Monte Carlo simulation; Reliability analysis; Earth pressure; Shallow foundations
类别
资金
- Natural Science Foundation of China [41102174]
- National 973 Basic Research Program of China [2011CB013800, 2011CB013506]
- Program for Changjiang Scholars and Innovative Research Team in University (PCSIRT) [IRT1029]
- Research Grants Council of the Hong Kong SAR [622308]
- Program for Young Excellent Talents in Tongji University
Geotechnical models are usually associated with considerable amounts of model uncertainty. In this study, the model uncertainty of a geotechnical model is characterised through a systematic comparison between model predictions and past performance data. During such a comparison, model input parameters (such as soil properties) may also be uncertain, and the observed performance may be subjected to measurement errors. To consider these uncertainties, the model uncertainty parameters, uncertain model input parameters and actual performance variables are modelled as random variables, and their distributions are updated simultaneously using Bayes' theorem. When the number of variables to update is large, solving the Bayesian updating problem is computationally challenging. A hybrid Markov Chain Monte Carlo simulation is employed in this paper to decompose the high-dimensional Bayesian updating problem into a series of updating problems in lower dimensions. To increase the efficiency of the Markov chain, the model uncertainty is first characterised with a first order second moment method approximately, and the knowledge learned from the approximate solution is then used to design key parameters in the Markov chain. Two examples are used to illustrate the proposed methodology for model uncertainty characterisation, with insights, discussions, and comparison with previous methods. (C) 2012 Elsevier Ltd. All rights reserved.
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