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Finite element model updating is challenging because 1) the problem isoftentimes underdetermined while the measurements are limited and/orincomplete; 2) many combinations of parameters may yield responses that aresimilar with respect to actual measurements; and 3) uncertainties inevitablyexist. The aim of this research is to leverage upon computational intelligencethrough statistical inference to facilitate an enhanced, probabilistic finiteelement model updating using incomplete modal response measurement. This newframework is built upon efficient inverse identification through optimization,whereas Bayesian inference is employed to account for the effect ofuncertainties. To overcome the computational cost barrier, we adopt Markovchain Monte Carlo (MCMC) to characterize the target function/distribution.Instead of using single Markov chain in conventional Bayesian approach, wedevelop a new sampling theory with multiple parallel, interactive and adaptiveMarkov chains and incorporate into Bayesian inference. This can harness thecollective power of these Markov chains to realize the concurrent search ofmultiple local optima. The number of required Markov chains and theirrespective initial model parameters are automatically determined via MonteCarlo simulation-based sample pre-screening followed by K-means clusteringanalysis. These enhancements can effectively address the aforementionedchallenges in finite element model updating. The validity of this framework issystematically demonstrated through case studies.

Enhanced Bayesian Model Updating with Incomplete Modal Information Using Parallel, Interactive and Adaptive Markov Chains