Validation of virtual sensing for the reconstruction of stresses in a railway bridge using field data of the KW51 bridge. (1st May 2023)

• Record Type: Journal Article
• Authors: Maes, K.; Lombaert, G.
• Is Part Of: Mechanical systems and signal processing. Volume 190(2023)
• Publication Date: 2023-05-01
• Subjects: Structural health monitoring -- Virtual sensing -- Digital twin -- Modeling error -- Validation; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2023.110142
• Languages: English
• ISSNs: 0888-3270

Abstract

Abstract: Virtual sensing enables a model-based extrapolation of the response measured in a limited number of locations (accelerations, strains, etc.) to other points of interest on the structure. One possible application of virtual sensing consists of estimating the stresses in critical but unmeasured details for assessing fatigue accumulation of structures exposed to cyclic or dynamic loading. Since virtual sensing involves the combined use of response data and a model of the structure, the outcome is inherently prone to modeling errors. This paper investigates the use of virtual sensing for stress/strain monitoring on steel railway bridges. The analysis is based on strain measurements obtained from an in situ monitoring campaign on railway bridge KW51 in Leuven, Belgium. It is found that the quasi-static response of the structure is largely affected by the behavior of the pot bearings, which cannot be easily modeled. A methodology is proposed to avoid the incorporation of the bearings in the model adopted in the virtual sensing, where the strain data are extended by longitudinal displacement measurements at the bearings. It is shown that this methodology enables accurate strain prediction, where in this case similar results are obtained for a model based on the blueprints only and a model calibrated based on experimental modal data. Highlights: Virtual sensing enables monitoring fatigue in unmeasured details. The results, however, are prone to modeling errors. A validation is performed using data obtained from strain monitoring on railway bridge KW51. Accurate strain estimation requires explicitly accounting for unknown bearing deformation.