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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]

Summary: Measurement of two‐dimensional surface displacement/strain distributions can be crucial in monitoring important structures. Computer vision techniques have the potentials for measuring surface displacements/strains. Conventional digital‐image‐correlation (DIC)‐based computer vision techniques that have been applied in controlled conditions with artificially painted speckle patterns, however, have difficulties in robust measurement of structures' surface displacements against optical noises. Additionally, surface displacements obtained by DIC based on block‐resolution template matching have limited accuracy because of low spatial resolutions and low level of smoothness. Therefore, a new computer vision technique SurfaceVision is proposed for accurate and robust surface displacement/strain measurement to tackle simulated field environmental conditions by incorporating multiple novel algorithms. First, a gradient‐based edge‐enhanced transform (EET) originally developed for one‐point displacement tracking is extended for enabling robust surface displacement measurements against optical noises by manipulating gradient information rather than image intensities. Then, the improvement in the smoothness of surface displacements is enabled by incorporating EET with the iterative displacement optimization and the customized smart branching algorithms. Moreover, a novel pixel‐resolution measurement algorithm is proposed for increasing the spatial resolution of surface displacements. Finally, an original intuitive strain conversion algorithm is developed for converting surface displacements into surface strains based on the principle similar to strain‐gauge transducers. The performance of SurfaceVision is first validated in the numerical simulation and further demonstrated in the experiment of the four‐point bending test. And a new method developed for predicting crack formations before they appear on structure surfaces, based on analyses of surface displacements/strains, is demonstrated. [ABSTRACT FROM AUTHOR]