This paper describes a new global damage identification framework for the continuous/periodic monitoring of civil structures. In order to localize and estimate the severity of damage regions, a one-stage model-based Bayesian probabilistic damage detection approach is proposed. This method, which is based on the response power spectral density of the structure, enjoys the advantage of broadband frequency information and can be implemented on input-output as well as output-only damage identification studies. A parallel genetic algorithm is subsequently used to evolve the optimal model parameters introduced for different damage conditions. Given the complex search space and the need to perform multiple time-consuming objective function evaluations, a parallel meta-heuristic provides a robust optimization tool in this domain. It is shown that this approach is capable of detecting structural damage in both noisy and noise-free environments.
