Fri, 2012-06-22 14:27 | 
The subject of current PhD thesis is the development of methods for the modeling and management of uncertainties in structural simulations.Emphasis is given on developing the probabilistic-statistical framework that is used for (a)quantifying and calibrating uncertainty models of mechanical systems and external excitation forces, in structural dynamics based on vibration measurements, as well as (b)propagating these modeling uncertainties in structural dynamics simulations to achieve updated robust predictions of system performance, reliability and safety. The tools for identifying system and uncertainty models as well as performing robust prediction analyses are the Bayesian Inference, Laplace methods of asymptotic approximation and more accurate stochastic simulation algorithms, such as Transitional MCMC]. These tools involve solving optimization problems, generating samples for tracing and then populating the important uncertainty region in the parameter space, as well as evaluating integrals over high-dimensional spaces of the uncertain model parameters. A moderate to very large number of repeated system analyses are required to be performed over the space of uncertain parameters. Consequently, the computational demands depend highly on the number of system analyses and the time required for performing a system analysis. For such large-order finite element models the computational demands in implementing asymptotic approximations as well as stochastic simulation techniques may be excessive .This study integrates an efficient Component Mode Synthesis technique that takes into account the FE model parameterization to substantially alleviate the computational burden associated with the Bayesian methodology .Another innovative aspect of this thesis is the use of measurements in order to improve the mathematical models that simulate the mechanical system, the excitation and the uncertainties that arise,based on the Bayesian Inference.Finally, the computational efficiency of the proposed techniques is demonstrated through applications (a) in structural health monitoring, damage identification and updating structural reliability of civil infrastructure, and (b) in predicting fatigue for metallic structures through a limited number of acceleration measurements.
