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In contrast to the traditional uncertainty quantification of the modal parameters identified from the output‐only measurement data, this study develops an uncertainty propagation and quantification method for structural flexibility identified from input–output measurement data and then uses the derived flexibility matrix for structural reliability analysis. First, a novel procedure for variance estimations of the complete system matrix of the state‐space model is derived based on first‐order perturbation theory. Then, the mode scaling factors and integrated modal flexibility are subsequently derived and quantified. Finally, the predicted displacements and their uncertainty intervals are used to implement the reliability analysis in terms of the structural displacement serviceability limit state (SLS). The accuracies of the derived expressions are verified by a numerical example through the comparison of variance values estimated from Monte Carlo simulations. The simulation example also indicates that we can expect to identify structural parameters with higher reliability if the input force to the structure is measured, and the identified modal scaling and corresponding flexibility matrix undergo more uncertainty than those of the modal parameters. Finally, we apply the proposed uncertainty quantification method to a scaled three‐story frame structure to demonstrate its practicability. The proposed method bridges a gap between the identified structural flexibility and structural reliability analysis directly by the probability category. [ABSTRACT FROM AUTHOR]

A novel numerical approach is presented, in the time domain, to simultaneously identify structural parameters and unmeasured input loadings using incomplete output measurement only. The identification problem is formulated as an optimization process, wherein the objective function is defined as the discrepancy between the measured and the predicted data, and is solved by a damped Gauss-Newton method. Because the proposed algorithm is a time domain technique, forward analyses are required to obtain predicted system responses so as to compute the discrepancy. Therefore, we propose an input force estimation scheme in the identification process to complete the task of input-output forward analyses, for the case of output-only measurement. The relationship between the unknown input loadings and the output measurement is established through a state space model, which basically formulates an ill-posed least squares problem. A statistical Bayesian inference-based regularization technique is presented to solve such a least squares problem. Finally, the proposed approach is illustrated by both numerical and experimental examples using output-only measurements of either acceleration or strain time histories. The results clearly show the robustness and the applicability of the proposed algorithm to simultaneously identify structural parameters and unmeasured input loadings with a high accuracy. [ABSTRACT FROM AUTHOR]