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|Title:||Damage identification of civil structures via response reconstruction||Authors:||Zhang, Chaodong||Degree:||Ph.D.||Issue Date:||2017||Abstract:||To ensure the safety and functionality of civil structures, it is vital to detect and quantify structural deteriorations in a timely manner for remedial work, before these deteriorations propagate and become detrimental to the entire structure. Structural health monitoring has been developing in past two decades, aiming to continually monitor loading conditions and assess structural conditions so that prompt decisions can be made if any damage is detected and quantified. Vibration-based damage identification, as a significant focus for structural condition assessment and health monitoring, has been the subject of many research efforts in recent years. Despite considerable progress in the structural health assessment of oil rigs, mechanical systems, and aerospace structures, such assessment still encounters some obstacles when it is applied to complex civil structures. These barriers mainly exhibit the following aspects: (1) the limited number of sensors compared to the number of structural components, which makes it difficult to capture enough dynamic responses for structural condition assessment; (2) the significance of model error, which is due to the difficulty of establishing a finite element (FE) model to represent the real dynamic behavior of large and complex structures; (3) the ill-posedness and non-identifiability of the inverse problem, which is due to the significant dimension of damaged areas corresponding to the numerous structural components; (4) the fact that the higher mode vibrations of cumbersome civil structures, which are more sensitive to local damage, are not easily excited under operating conditions; and (5) the enormous, unaffordable computational demand associated with the large dimensions of discretized FE model matrices and the scope of dynamic FE analysis during iterations.
Taking these obstacles into consideration, this thesis aims to develop novel vibration-based damage identification methods specifically suited for application to civil structures of large-scale and geometric complexity. To accomplish this aim, the following research efforts have been made. First, an optimal multi-type sensor placement method is proposed for the best response reconstruction on the location where no sensors are installed as well as the time evolution of the pre-located deterministic excitations; Second, sparse regularization is proposed to replace the traditional Tikhonov regularization in finite element (FE) model updating based damage identification. The superiority of sparse regularization is highlighted not only in constraining the solution norm but also in promoting solution sparsity. Third, the Kalman filter based response reconstruction and multi-type response fusion are integrated with the sparse regularized FE model updating to supplement the limitation of sensor measurements as well as take the benefits of both global and local response fusion in structural damage identification. Fourth, the proposed response reconstruction-oriented damage identification strategy is extended from circumstances where the time evolution of external excitations is required to the conditions where external excitations acting on the structure are unknown. And finally, multi-level damage identification via response reconstruction is proposed for identification of element-level damages on large civil structures. This method firstly detects possible damaged regions over the condensed and assembled substructures through dynamic substructuring technique using measured responses; then, the damage at the element level is further localized and quantified over suspicious substructures using the reconstructed responses of the identified substructures. As a result, the damage dimensions are dramatically decreased in the identification of both levels; the computational efforts are dramatically decreased, while the identifiability of the damage is increased. Besides the theoretical studies, numerical studies and experimental investigations are also conducted to demonstrate the procedures of these proposed methods and verify their effectiveness. Simulation studies and experimental investigations are performed on an overhanging beam of 40 elements on the following issues: optimal sensor placement for joint response and excitation reconstruction, sparse regularization in FE model updating based damage identification, and damage identification via response reconstruction under both known and unknown excitations. Performance evaluation of the proposed optimal sensor placement method for joint response and excitation reconstruction as well as multi-level damage identification via response reconstruction is also conducted through experimental investigation on the long-span Tsing Ma suspension bridge testbed at the Structural Dynamic Laboratory of The Hong Kong Polytechnic University. The research works presented and the results obtained in this thesis contribute to the damage identification of the major civil structures.
|Subjects:||Structural health monitoring.
Structural analysis (Engineering)
Hong Kong Polytechnic University -- Dissertations
|Pages:||xxxviii, 343 pages : color illustrations|
|Appears in Collections:||Thesis|
View full-text via https://theses.lib.polyu.edu.hk/handle/200/8875
Citations as of May 28, 2023
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