Structural system identification by dynamic observability technique
- Autores: Tian Peng
- Directores de la Tesis: José Turmo Coderque (dir. tes.), Joan Ramón Casas Rius (codir. tes.)
- Lectura: En la Universitat Politècnica de Catalunya (UPC) ( España ) en 2021
- Idioma: español
- Materias:
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- Resumen
Structure system identification (SSI) can be classified as static and dynamic depending on the type of excitation. SSI by Observability Method (OM) using static tests was proposed and analyzed to address the observability of the estimated parameters. This mathematical approach has been used in other fields such as hydraulics, electrical, and power networks or transportation. Usually, the structural behavior of engineering structures can be identified according to dynamic characteristics such as mode shapes, natural frequencies, and damping ratios. However, the analysis of SSI by dynamic Observability Method using dynamic information is lacking.
This Ph.D. thesis developed the dynamic Observability Method using masses, modal frequencies, modal deflections based on the static OM to obtain the geometrical and mechanical parameters of the structure. This thesis mainly contains three aspects of work.
Firstly, in chapter 3, the development, for the first time, of constrained observability techniques (COM) for parametric estimation of structures using dynamic information such as frequencies and mode-shapes was proposed. New algorithms are introduced based on the dynamic eigenvalue equation. Two step by step examples are used to illustrate the functioning of these. Parametric expressions for the observed variables are successfully obtained,which will allow the study of the sensitivity of each of the variables in the problem and the error distribution, which is an advantage with respect to non-parametric SSI techniques. A large structure is used to validate this new application, whose structural properties can be obtained satisfactorily in either the whole or local analysis, and the results show that the required measurement set is smaller than the required for a static analysis. Chapters 4 and 5 are the applications of COM to fill the shortcomings of current research, such as the optimal SHM+SSI strategy and uncertainty quantification.
Secondly, in chapter 4, the role of the SHM strategy and the SSI analysis based on the Constrained Observability Method (COM), which aims at reducing the estimation error, is discussed. A machine learning decision tool to help building the best-combined strategy of SHM and SSI that can result in the most accurate estimations of the structural properties is proposed, and the combination of COM and decision tree algorithm is used for the first time. The machine learning algorithm is based on the theory of Decision Trees. Decision trees are firstly presented to investigate the influence of the variables (layout of bridge, span length, measurement set, and weight factor in the objective function of the COM) involved in the SHM+SSI process on the error estimation in a general structure. The verification of the method with a real bridge with different levels of damage shows that the method is robust even for a high damage level, showing the SHM+SSI strategy that yields the most accurate estimation.
Finally, an analysis of uncertainty quantification (UQ) is necessary to assess the effect of uncertainties on the estimated parameters and to provide a way to evaluate these uncertainties. This work is carried out in chapter 5. There are a large number of UQ approaches in science and engineering. It is identified that the proposed dynamic Constrained Observability Method (COM) can make up for some of the shortcomings of existing methods. After that, the COM is used to analyze a real bridge. A result is compared with a method based on a Bayesian approach demonstrating its applicability and correct performance through the analysis of a reinforced concrete beam.
