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Finite Element Model Updating in Time Domain Using Water Cycle Algorithm

Dehghanpour, Fatemeh | 2019

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 51879 (09)
  4. University: Sharif University of Technology
  5. Department: Civil Engineering
  6. Advisor(s): Rahimzadeh Rofooei, Fayaz; Mahdavi, Hossein
  7. Abstract:
  8. Due to inevitable uncertain sources in modeling, operational and environmental conditions, finite element model and structure’s response to a same load pattern differs drastically. To reduce this difference and make the model’s response close to the real structure, finite element model updating procedure is essential. Updated model can be used for structural assessment, damage identification, remaining service life estimation, and structural control. Model updating methods are categorized into two groups in term of information domain used for model updating; time-domain methods and frequency-domain approaches. Time-domain methods have a preference because of the main drawbacks of frequency domain methods such as low reliability in high mode information, consequentially low sensitivity to local damage and so forth. Furthermore, frequency-domain methods are not practical for non-linear structures. On the other hand, methods for finite element model updating those refer to the minimization of the difference of simulated and measured responses are classified into classical methods and non-classical methods. Classical methods are gradient based and they try to reduce the error and find structure’s parameters through a point-to-point search. Therefore, these methods easily converge to the local minima in the presence of noise. Moreover, ill-condition matrixes make model updating impossible when the number of degree of freedom of structure increases. Non-classical methods start searching by a population of initial guess and metaheuristic rules instead of gradient information. Therefore, the global convergence has a high probability and this constitutes the main merit of these approaches over classical methods.In this thesis, time-domain finite element model updating will be done using the recently developed water cycle algorithm known as a metaheuristic and evolutionary algorithm. The effect of noise, incomplete measurement, and data length will be investigated for the resulted convergence and accuracy. Structural identification of large structures will be done using substructure method and results will be compared with the result of two well-known methahuresic methods, namely, improved genetic algorithm, and particle swarm optimization. Due to the robustness of water cycle algorithm in referring to the balanced exploration and exploitation capacity to search in the feasible domain, it is concluded to achive a reasonable accuracy with a fast convergence
  9. Keywords:
  10. Model Updating ; Structural Identification ; Time Domain Method ; Improved Genetic Algorithm ; Particles Swarm Optimization (PSO) ; Water Cycle Algorithm

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