In the last twenty years vibration-based methods for Structural Health Monitoring (SHM) have received increasing attention by both academics and operators, due to undoubtable advantages they provide for damage identification purposes. These are mainly related to the capability of providing continuous information about the global state of the structure without a prior knowledge about the location of possible damages and without the need to access the damaged portion of the structure. These methods rely on the fact that a damage inducing a loss of stiffness results in a change of the dynamic behavior therefore, structural responses to forced or ambient vibrations can be used to retrieve information about these changes. Despite the large amount of literature published on these methods, their experimental validation is often limited to highly controlled laboratory conditions or numerical simulations. The validation of the algorithms on real damaged structures is often hampered by the unavailability of data and this constitutes indeed a challenge for the implementation of these techniques at the operational level. In the first part of this paper the possible drawbacks related to the effect of uncertainties related to the effect of environmental sources, noise in hardware systems for the acquisition and transmission of structural responses and approximations in the adopted models. Another aspect that has slow down the practical diffusion of these methods, and generally of SHM techniques, is the difficulty to quantify their benefits prior to their implementation. This has sometime restraint the operators from investing on them, despite the several advantages these systems offer in terms of maintenance optimization and emergency management. In the paper some recent research efforts on several aspects related to the development and implementation of these methods are illustrated.

Vibration-based structural health monitoring: Challenges and opportunities

Limongelli M. P.
2019-01-01

Abstract

In the last twenty years vibration-based methods for Structural Health Monitoring (SHM) have received increasing attention by both academics and operators, due to undoubtable advantages they provide for damage identification purposes. These are mainly related to the capability of providing continuous information about the global state of the structure without a prior knowledge about the location of possible damages and without the need to access the damaged portion of the structure. These methods rely on the fact that a damage inducing a loss of stiffness results in a change of the dynamic behavior therefore, structural responses to forced or ambient vibrations can be used to retrieve information about these changes. Despite the large amount of literature published on these methods, their experimental validation is often limited to highly controlled laboratory conditions or numerical simulations. The validation of the algorithms on real damaged structures is often hampered by the unavailability of data and this constitutes indeed a challenge for the implementation of these techniques at the operational level. In the first part of this paper the possible drawbacks related to the effect of uncertainties related to the effect of environmental sources, noise in hardware systems for the acquisition and transmission of structural responses and approximations in the adopted models. Another aspect that has slow down the practical diffusion of these methods, and generally of SHM techniques, is the difficulty to quantify their benefits prior to their implementation. This has sometime restraint the operators from investing on them, despite the several advantages these systems offer in terms of maintenance optimization and emergency management. In the paper some recent research efforts on several aspects related to the development and implementation of these methods are illustrated.
2019
Advances in Engineering Materials, Structures and Systems: Innovations, Mechanics and Applications - Proceedings of the 7th International Conference on Structural Engineering, Mechanics and Computation, 2019
9780429426506
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1186189
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