Structural Engineering and Mechanics

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Evolutionary-base finite element model updating and damage detection using modal testing results

  • Vahidi, Mehdi (School of Civil Engineering, College of Engineering, University of Tehran) ;
  • Vahdani, Shahram (School of Civil Engineering, College of Engineering, University of Tehran) ;
  • Rahimian, Mohammad (School of Civil Engineering, College of Engineering, University of Tehran) ;
  • Jamshidi, Nima (School of Civil Engineering, College of Engineering, University of Tehran) ;
  • Kanee, Alireza Taghavee (School of Civil Engineering, College of Engineering, University of Tehran)
  • Received : 2018.03.14
  • Accepted : 2019.02.19
  • Published : 2019.05.10
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Abstract

This research focuses on finite element model updating and damage assessment of structures at element level based on global nondestructive test results. For this purpose, an optimization system is generated to minimize the structural dynamic parameters discrepancies between numerical and experimental models. Objective functions are selected based on the square of Euclidean norm error of vibration frequencies and modal assurance criterion of mode shapes. In order to update the finite element model and detect local damages within the structural members, modern optimization techniques is implemented according to the evolutionary algorithms to meet the global optimized solution. Using a simulated numerical example, application of genetic algorithm (GA), particle swarm (PSO) and artificial bee colony (ABC) algorithms are investigated in FE model updating and damage detection problems to consider their accuracy and convergence characteristics. Then, a hybrid multi stage optimization method is presented merging advantages of PSO and ABC methods in finding damage location and extent. The efficiency of the methods have been examined using two simulated numerical examples, a laboratory dynamic test and a high-rise building field ambient vibration test results. The implemented evolutionary updating methods show successful results in accuracy and speed considering the incomplete and noisy experimental measured data.

Keywords

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