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Summary Structural model updating by estimation of stiffness and mass parameters via monitoring of dynamic characteristics has attracted much attention in recent decades. In this study, frequency response functions (FRF) are utilized in order to identify unknown structural parameters using a sensitivity‐based model updating approach. A sensitivity equation that diminishes adverse effects of incompleteness of FRF data is proposed for model updating. Efficiency of the proposed method and impacts of measurement errors and incompleteness of measured data are examined numerically through a truss reference example. The stiffness and mass parameters of the intact model are updated using the damped FRFs of the simulated damaged model. The results demonstrate that the proposed method is capable of precisely identifying the location and the severity of damage in all studied cases. Copyright © 2015 John Wiley & Sons, Ltd.

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Vibration-based damage assessment approaches use modal parameters, such as frequency response functions, mode shapes, and natural frequencies, as indicators of structural damage. Nevertheless, these parameters are sensitive not only to damage but also to temperature variations. Most civil engineering structures are exposed to varying environmental conditions, thus hindering vibration-based damage assessment. Therefore, in this article, a new damage assessment algorithm based on pattern recognition is proposed to scrutinize the healthy state of a structure in the presence of uncertainties such as noise and temperature. The algorithm adopts a combination of couple sparse coding and deep neural network as an ensemble system to assess damage. The proposed method is validated using a numerical model of a truss bridge and experimental data of the I-40 bridge. The results demonstrate its efficiency in the localization and quantification of damages under varying temperature conditions.

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