A Hybrid Particle Swarm Optimization and Genetic Algorithm for Model Updating of A Pier-Type Structure Using Experimenta
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rticle Swarm Optimization and Genetic Algorithm for Model Updating of A Pier-Type Structure Using Experimental Modal Analysis Alireza MOJTAHEDIa, *, Shahriar BAYBORDIb, Amin FATHIb, Aliakbar YAGHUBZADEHb a Department of Water Resources Engineering, Faculty of Civil Engineering, University of Tabriz, Tabriz 51666-
16471, Iran b Department of Maritime Engineering, Amirkabir University of Technology, Tehran, Iran
Received March 5, 2020; revised June 16, 2020; accepted July 20, 2020 ©2020 Chinese Ocean Engineering Society and Springer-Verlag GmbH Germany, part of Springer Nature Abstract Conventional design of pier structures is based on the assumption of fully rigid joints. In practice, the real connections are semi-rigid that cause changes in dynamic characteristics. In this study, quality of the joints is investigated by considering changes in natural frequencies. For this purpose, numerical and experimental modal analyses are carried out on related physical model of a pier type structure. When numerical results are evaluated, natural frequencies generally do not match the expected experimental results. Uncertainties in different aspects of engineering problems are always a challenge for researchers. The numerical models which are constructed on the basis of highly idealized scheme may not be able to represent all of the physical aspects of the physical one. For this study, determination of percentage of semi-rigid joints is considered as an optimization problem based on the numerical and experimental frequencies. Probabilistic sensitivity analysis is also used to determine the search space. A new technique of optimization problem is solved by a combination of smart particle swarm optimization (PSO) and genetic algorithms, and a complicated and efficient system for model updating process is introduced. It is observed that the hybrid PSO-Genetic algorithm is applicable and appropriate in model updating process. It performs better than PSO algorithm, considering the good agreement between theoretical frequencies and experimental ones, before and after model updating. Key words: pier structure, probabilistic sensitivity analysis, hybrid PSO-Genetic algorithm, dynamic characteristics Citation: Mojtahedi, A., Baybordi, S., Fathi, A., Yaghubzadeh, A., 2020. A hybrid particle swarm optimization and genetic algorithm for model updating of a pier-type structure using experimental modal analysis. China Ocean Eng., 34(5): 697–707, doi: https://doi.org/10.1007/s13344-0200060-2
1 Introduction Investigation on the behavior of the coastal structures needs an accurate study on the complicated dynamic conditions arise from the harsh marine environment. Inadequate knowledge of the dynamic conditions in different situations may lead to major structural damages. About the coastal structures, in view of the unexpected aspects arising from the marine environment, there are numerous
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