Geotechnical engineers are always faced with uncertainties and spatial variations in
material parameters. In this work, we propose to develop a framework able to account
for different types of uncertainties in a formal and logical manner, to incorporate all
available sources of information, and to integrate the uncertainty in an estimate of the
probability.
In geotechnical engineering, current soil classification charts based on CPT data
may not provide an accurate prediction of soil type, even though soil classification is an
essential component in the design process. As a cheaper and faster alternative to sample
retrieval and testing, field methods such as the cone penetration test (CPT) can be used.
A probabilistic soil classification approach is proposed here to improve soil
classification based on CPT. The proposed approach provides a simple and
straightforward tool that allows updating the soil classification charts based on sitespecific
data.
In general, settlements can be the result of surface loads or variable soil deposits.
In current practice, the analysis to determine settlements is deterministic. It assumes that the soil profile at a site is uniform from location to location, and only allows limited
consideration of the variations of the material properties and initial conditions within soil
layers in spite of the wide range of compositions, gradations, and water contents in
natural soils. A Bayesian methodology is used to develop an unbiased probabilistic
model that accurately predicts the settlements and accounts for all the prevailing
uncertainties. The proposed probabilistic model is used to estimate the settlements of
the foundation of a structure in the Venice Lagoon, Italy. The conditional probability
(fragility) of exceeding a specified settlement threshold for a given vertical pressure is
estimated. A predictive fragility and confidence intervals are developed with special
attention given to the treatment and quantification of aleatory and epistemic
uncertainties. Sensitivity and importance measures are computed to identify the key
parameters and random variables in the model.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-2009-05-735 |
| Date | 2009 May 1900 |
| Creators | Jung, Byoung C. |
| Contributors | Biscontin, Giovanna, Gardoni, Paolo |
| Source Sets | Texas A and M University |
| Language | English |
| Detected Language | English |
| Type | Book, Thesis, Electronic Dissertation, text |
| Format | application/pdf |
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