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Summary: This paper proposes a novel approach for structural damage identification with a limited number of modal measurements and ultra‐sparse Bayesian regression. An iterative Cross Modal Strain Energy (CMSE) method is proposed for establishing the linear regression model. It can be applied to enlarge the number of available modes, thus alleviating the problem of insufficient mode orders. In addition, the proposed approach can also be used to solve the identification of incomplete measurements by combining with a dynamic condensation process. The condensed system matrices of the damaged structure are updated iteratively by the identified damage severity vector. A major contribution of this study is that the most advanced Bayesian linear regression estimator, called Horseshoe (HS), is first introduced to provide an ultra‐sparse regularization. Owning to the particular choice of a half‐Cauchy prior to the global and local scale hyper‐parameters, using HS can provide a sparser solution than the Bayesian lasso (BL). Therefore, this approach is extremely suitable for structural damage identification with sparse solutions in nature. Other advantages of using HS consist of the easy implementation of Gibbs sampling, effective convergence rate and hyper‐parameter tuning, good stability, and the ability to conduct the indeterminate inverse identification arising from insufficient mode order. The effectiveness and performance of the proposed approach are validated by numerical and experimental studies, considering the effects of measurement noise and a limited number of modal measurements with an insufficient number of mode orders and incomplete measurements. [ABSTRACT FROM AUTHOR]

Summary: The drive‐by bridge health monitoring is to assess the bridge condition using the acceleration responses measured on the body or axle of instrumented vehicles. The vehicle responses are greatly affected by the road surface roughness that makes the bridge dynamic information blurred. Instead of direct using vehicle responses for the bridge monitoring, the dynamic response of contact point (CP) between the vehicle and bridge is further explored to enhance the drive‐by bridge modal identification. A novel three‐step framework is proposed to extract the components related to the bridge vibration from vehicle responses. The first step is to identify the input forces of two successive vehicles by solving the combined state‐input estimation problem using dual Kalman filter. The CP responses of two contact points are calculated using the input forces and vehicle parameters. In the second step, the subtraction technique is applied to the identified CP responses of the two instrumented vehicles and the effect of the road surface roughness can be significantly reduced. Finally, an automatic singular spectrum analysis technique (auto SSA) is incorporated to decompose the response residual. Then the mono‐component modes related to the bridge response are extracted from the response residual for drive‐by bridge modal identification and/or the nonstationary characteristic identification of vehicle–bridge interaction (VBI) system. Results of numerical and experimental study demonstrate that the method can significantly suppress the vehicle response component and reduce the effect of road surface roughness to enhance the bridge modal identification. [ABSTRACT FROM AUTHOR]

The cover image is based on the Research Article I Structural damage identification with limited modal measurements and ultra-sparse Bayesian regression i by Mingqiang Xu et al., https://doi.org/10.1002/stc.2729. GLO:VUV/01jun21:stc2778-toc-0001.jpg PHOTO (COLOR): . gl. [Extracted from the article]