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This paper is concerned with the inverse identification of the stress state in axially loaded slender members of iron and steel truss structures using measured dynamic data. A methodology is proposed based on the finite element model updating coupled with nature-inspired optimization techniques, in particular the particle swarm optimization. The numerical model of truss structures is calibrated using natural frequencies and mode shapes from vibration tests, as well as additional information of the axial forces in selected truss members based on the experimentally identified modal parameters. The results of the identification are the axial forces or corresponding stresses in truss structures and the joint rigidity in relation to pinned and rigid conditions. Attention is given to several examined aspects, including the effects of the axial tensile and compressive forces on the dynamic responses of trusses, mode pairing criteria, as well as modeling assumptions of joints and the use of a joint rigidity parameter. Considering the pairing of modes, it is performed by adapting an enhanced modal assurance criterion that allows the selection of desired clusters of degrees-of-freedom. Thus, information extracted from the measurements related to specific modes is utilized in a more beneficial way. For modeling of joints, the numerical model of a truss structure includes rotational springs of variable stiffness to represent semi-rigid connections. Moreover, a fixity factor is introduced for practical estimation of the joint flexibility. The effectiveness of the proposed methodology is demonstrated by case studies involving simulated and laboratory experimental data.

Non-destructive Assessment of the Axial Stress State in Iron and Steel Truss Structures by Dynamic Measurements