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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.
Vibration-based model updating and identification of multiple axial forces in truss structures
(2017)
Safety assessment of existing iron and steel truss structures requires the determination of the axial Forces and corresponding stresses in truss structural members. The results of the axial force determination can be integrated as part of a structural health Monitoring scheme for existing trusses. In this work, a methodology is proposed to identify multiple axial forces in members of a truss structure based on the modal parameters. Vibration test allows the identification of the natural frequencies and mode shapes, globally of the truss structure as well as locally of the individual bars. The method calibrates the numerical model of the truss structure using a genetic algorithm and strategic validation criteria. The validation criteria are based on the identified natural frequencies and global mode shapes of the truss structure as well as information of the axial forces in the individual bars of the truss, which are estimated from the natural frequencies and five amplitudes of the corresponding local mode shapes of the single bars based on an analytical-based algorithm. The calibration allows the identification of the axial forces in all bars of the truss structure. For mode pairing strategy, a technique makes use of the enhanced modal assurance criteria with the calculation of the modal strain energies.
Moreover, the modal strain energies are also used to select the relevant local mode shape of the individual bars. The feasibility and accuracy of the proposed methodology is verified by laboratory experiments on several truss structures. In situ tests on existing trusses are intended. The results from one of the laboratory tested structures, i.e. a two-bar system, are included in this paper.
One of the main challenges regarding our civil infrastructure is the efficient operation over their complete design lifetime while complying with standards and safety regulations. Thus, costs for maintenance or replacements must be optimized while still ensuring specified safety levels. This requires an accurate estimate of the current state as well as a prognosis for the remaining useful life. Currently, this is often done by regular manual or visual inspections within constant intervals. However, the critical sections are often not directly accessible or impossible to be instrumented at all. Model‐based approaches can be used where a digital twin of the structure is set up. For these approaches, a key challenge is the calibration and validation of the numerical model based on uncertain measurement data. The aim of this contribution is to increase the efficiency of model updating by using the advantage of model reduction (Proper Generalized Decomposition, PGD) and applying the derived method for efficient model identification of a random stiffness field of a real bridge.”
Using digital twins for decision making is a very promising concept which combines simulation models with corresponding experimental sensor data in order to support maintenance decisions or to investigate the reliability. The quality of the prognosis strongly depends on both the data quality and the quality of the digital twin. The latter comprises both the modeling assumptions as well as the correct parameters of these models. This article discusses the challenges when applying this concept to realmeasurement data for a demonstrator bridge in the lab, including the data management, the iterative development of the simulation model as well as the identification/updating procedure using Bayesian inference with a potentially large number of parameters. The investigated scenarios include both the iterative identification of the structural model parameters as well as scenarios related to a damage identification. In addition, the article aims at providing all models and data in a reproducibleway such that other researcher can use this setup to validate their methodologies.