01914nas a2200169   4500000000100000008004100001260001200042100002200054700002600076700002000102700002000122700002000142700002200162245011400184856006400298520138200362       2023                            d  c01/20231 aMariusz Ostrowski1 aBartlomiej Blachowski1 aBartosz Wójcik1 aMateusz Żarski1 aPiotr Tauzowski1 aŁukasz Jankowski00aA framework for computer vision-based health monitoring of a truss structure subjected to unknown excitations  uhttps://link.springer.com/article/10.1007/s11803-023-2154-33 a<p>Computer vision (CV) methods for measurement of structural vibration are less expensive, and their application is more straightforward than methods based on sensors that measure physical quantities at particular points of a structure. However, CV methods produce significantly more measurement errors. Thus, computer vision-based structural health monitoring (CVSHM) requires appropriate methods of damage assessment that are robust with respect to highly contaminated measurement data. In this paper a complete CVSHM framework is proposed, and three damage assessment methods are tested. The first is the augmented inverse estimate (AIE), proposed by Peng et al. in 2021. This method is designed to work with highly contaminated measurement data, but it fails with a large noise provided by CV measurement. The second method, as proposed in this paper, is based on the AIE, but it introduces a weighting matrix that enhances the conditioning of the problem. The third method, also proposed in this paper, introduces additional constraints in the optimization process; these constraints ensure that the stiff ness of structural elements can only decrease. Both proposed methods perform better than the original AIE. The latter of the two proposed methods gives the best results, and it is robust with respect to the selected coefficients, as required by the algorithm.</p>