Time-domain structural model updating following the Bayesian approach in the absence of system input information

The modal-domain structural model updating and damage detection methods own the advantage that they perform the whole computation without the information of system input, when compared to the time-domain. The system input required in the time-domain methods is hardly measured in realistic field tests of civil engineering structures. However, the time-domain methods own the advantage that they carry out model updating based on raw measured dynamic responses but not the processed (and compressed) modal parameters. The modal-domain methods only uses the compressed modal parameters, and some useful system information may be lost in the compressing process. Thus, higher errors and uncertainties are induced in the model updating results from the modal-domain methods. To take advantages and avoid disadvantages from the traditional time-domain and modal-domain model updating methods, a novel time-domain MCMC-based Bayesian model updating method without the information of the system input is developed in this paper by incorporating a newly developed dynamic response reconstruction technique and extending the formulation of the posterior probability density function (PDF) and the corresponding bridge PDF in the MCMC sampling process. A new modal decomposition algorithm is proposed in this paper for accurate modal response decomposition even in case of dense or sparse modes, and filters are not required for higher computation accuracy. Experimental case studies on a two-story steel frame were conducted to illustrate the proposed methodology in both structural model updating and damage detection. According to the analysis results, it is concluded that the proposed methodology outperforms the modal-domain methods especially in the cases with small number of measured DOFs. © 2024 Elsevier Ltd