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Uncertain Model Parameters (uncertain + model_parameter)

Distribution by Scientific Domains

Engineering	50%

Selected Abstracts

Structural Model Updating and Health Monitoring with Incomplete Modal Data Using Gibbs Sampler

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 4 2006
Jianye Ching
It is based on the Gibbs sampler, a stochastic simulation method that decomposes the uncertain model parameters into three groups, so that the direct sampling from any one group is possible when conditional on the other groups and the incomplete modal data. This means that even if the number of uncertain parameters is large, the effective dimension for the Gibbs sampler is always three and so high-dimensional parameter spaces that are fatal to most sampling techniques are handled by the method, making it more practical for health monitoring of real structures. The approach also inherits the advantages of Bayesian techniques: it not only updates the optimal estimate of the structural parameters but also updates the associated uncertainties. The approach is illustrated by applying it to two examples of structural health monitoring problems, in which the goal is to detect and quantify any damage using incomplete modal data obtained from small-amplitude vibrations measured before and after a severe loading event, such as an earthquake or explosion. [source]

Tangential-projection algorithm for manifold representation in unidentifiable model updating problems

EARTHQUAKE ENGINEERING AND STRUCTURAL DYNAMICS, Issue 4 2002
Lambros S. Katafygiotis
Abstract The problem of updating a structural model and its associated uncertainties by utilizing structural response data is addressed. In an identifiable case, the posterior probability density function (PDF) of the uncertain model parameters for given measured data can be approximated by a weighted sum of Gaussian distributions centered at a number of discrete optimal values of the parameters at which some positive measure-of-fit function is minimized. The present paper focuses on the problem of model updating in the general unidentifiable case for which certain simplifying assumptions available for identifiable cases are not valid. In this case, the PDF is distributed in the neighbourhood of an extended and usually highly complex manifold of the parameter space that cannot be calculated explicitly. The computational difficulties associated with calculating the highly complex posterior PDF are discussed and a new adaptive algorithm, referred to as the tangential-projection (TP) algorithm, allowing for an efficient approximate representation of the above manifold and the posterior PDF is presented. Using this approximation, expressions for calculating the uncertain predictive response are established. A numerical example involving noisy data is presented to demonstrate the proposed method. Copyright © 2002 John Wiley & Sons, Ltd. [source]

At what costs will screening with CT colonography be competitive?

INTERNATIONAL JOURNAL OF CANCER, Issue 5 2009
A cost-effectiveness approach
Abstract The costs of computed tomographic colonography (CTC) are not yet established for screening use. In our study, we estimated the threshold costs for which CTC screening would be a cost-effective alternative to colonoscopy for colorectal cancer (CRC) screening in the general population. We used the MISCAN-colon microsimulation model to estimate the costs and life-years gained of screening persons aged 50,80 years for 4 screening strategies: (i) optical colonoscopy; and CTC with referral to optical colonoscopy of (ii) any suspected polyp; (iii) a suspected polyp ,6 mm and (iv) a suspected polyp ,10 mm. For each of the 4 strategies, screen intervals of 5, 10, 15 and 20 years were considered. Subsequently, for each CTC strategy and interval, the threshold costs of CTC were calculated. We performed a sensitivity analysis to assess the effect of uncertain model parameters on the threshold costs. With equal costs ($662), optical colonoscopy dominated CTC screening. For CTC to gain similar life-years as colonoscopy screening every 10 years, it should be offered every 5 years with referral of polyps ,6 mm. For this strategy to be as cost-effective as colonoscopy screening, the costs must not exceed $285 or 43% of colonoscopy costs (range in sensitivity analysis: 39,47%). With 25% higher adherence than colonoscopy, CTC threshold costs could be 71% of colonoscopy costs. Our estimate of 43% is considerably lower than previous estimates in literature, because previous studies only compared CTC screening to 10-yearly colonoscopy, where we compared to different intervals of colonoscopy screening. © 2008 Wiley-Liss, Inc. [source]

Design for model parameter uncertainty using nonlinear confidence regions

AICHE JOURNAL, Issue 8 2001
William C. Rooney
An accurate method presented accounts for uncertain model parameters in nonlinear process optimization problems. The model representation is considered in terms of algebraic equations. Uncertain quantity parameters are often discretized into a number of finite values that are then used in multiperiod optimization problems. These discrete values usually range between some lower and upper bound that can be derived from individual confidence intervals. Frequently, more than one uncertain parameter is estimated at a time from a single set of experiments. Thus, using simple lower and upper bounds to describe these parameters may not be accurate, since it assumes the parameters are uncorrelated. In 1999 Rooney and Biegler showed the importance of including parameter correlation in design problems by using elliptical joint confidence regions to describe the correlation among the uncertain model parameters. In chemical engineering systems, however, the parameter estimation problem is often highly nonlinear, and the elliptical confidence regions derived from these problems may not be accurate enough to capture the actual model parameter uncertainty. In this work, the description of model parameter uncertainty is improved by using confidence regions derived from the likelihood ratio test. It captures the nonlinearities efficiently and accurately in the parameter estimation problem. Several examples solved show the importance of accurately capturing the actual model parameter uncertainty at the design stage. [source]