Vibration-based damage detection for structural connections using incomplete modal data by Bayesian approach and model reduction technique

Most of the existing damage detection methods focused on damage along members of the structure without considering possible damage at its connections. Under the Bayesian framework, the finite element (FE) model reduction technique and the system mode concept, this paper presents a practical method for structural bolted-connection damage detection using noisy incomplete modal parameters identified from limited number of sensors. Based on the incomplete modal identification results, the most probable structural model parameters, the most probable system eigenvalues and partial modes shapes together with the associated uncertainties can be identified simultaneously. There are several significant features of the proposed method: (1) it does not require computation of the system mode shapes for the full model due to the FE model reduction technique; (2) matching between measured modes and model predicted modes is avoided in contrast to most existing methods in the literature; and (3) an efficient iterative solution strategy is also proposed to resolve the difficulties arisen from the high-dimensional nonlinear optimization problem for the structural model parameters and the incomplete system modal parameters. Numerical simulations and experimental verifications of a four-storey two-bay bolt-connected steel frame and a two-storey laboratory bolted frame, respectively, are utilized to demonstrate the validity and efficiency of proposed methodology.