Non-probabilistic convex models need to be provided only the changing boundary of parameters rather than their exact probability distributions; thus, such models can be applied to uncertainty analysis of complex structures when experimental information is lacking. The interval and the ellipsoidal models are the two most commonly used modeling methods in the field of non-probabilistic convex mod...

Project planning inevitably involves uncertainty. The basic input parameters for planning (time, cost and resources for each activity) are not deterministic and are affected by various sources of uncertainty. Moreover, there is a causal relationship between these uncertainty sources and project parameters; this causality is ignored in current state-of-the-art project planning techniques (such a...

Significant uncertainty is known to be present in the internal radiation dose assessments for intakes of uranium compounds. The aim of this thesis was to investigate the sources of the uncertainty in the dose assessment and in particular the biokinetic models used to describe the movement of uranium in the body. A variety of numerical and statistical approaches have been used to explore the unc...

Parameters in climate models are usually calibrated manually, exploiting only small subsets of the available data. This precludes both optimal calibration and quantification uncertainties. Traditional Bayesian methods that allow uncertainty too expensive for models; they also not robust presence internal variability. For example, Markov chain Monte Carlo (MCMC) typically require model runs sens...

The use of orthonormal functions and consistency relationships to identify models of multivariable processes and their uncertainty descriptions, is discussed in this paper. The norm-bounded uncertainty can be obtained from the covariance matrix of the parameters. Using a-priori consistency relationships, unstructured uncertainty may be converted to a structured one. The μ analysis shows that th...

Successful application of dual permeability models (DPM) to predict contaminant transport is contingent upon measured or inversely estimated soil hydraulic and solute transport parameters. The difficulty in unique identification of parameters for the additional macropore- and matrix-macropore interface regions, and knowledge about requisite experimental data for DPM has not been resolved to dat...

Joint parameter identification is a key problem in the modeling of assembled complex structural. Dynamic behavior of mechanical joints may be with random feature due to the stochastic properties of preload, joint geometry, contact surface and its finishes, etc. A procedure for stochastic mechanical joint identification based on a novel model updating technique combined with probabilistic approa...