Genetic Algorithm Based Objective Functions Comparative Study for Damage Detection and Localization in Beam Structures

11th International Conference on Damage Assessment of Structures

物理学;材料科学

Samir, K.^1 ; Idir, B.^1 ; Serra, R.^2 ; Brahim, B.^1 ; Aicha, A.^1

University m'Hamed Bougara Boumerdes, LEMI Laboratory Research Team Modelling and Simulation in Mechanical Engineering, Boumerdes

35000, Algeria^1

Laboratoire de Mecanique et Rheologie INSA Centre Val de Loire, LMR, 3 Rue de la chocolaterie, Blois

41000, France^2

关键词: Detection and localization;    Detection techniques;    Different structure;    Modal assurance criterion;    Non destructive testing;    Objective functions;    Operational aspects;    Optimization problems;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/628/1/012035/pdf
DOI  :  10.1088/1742-6596/628/1/012035

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

The detection techniques based on non-destructive testing (NDT) defects are preferable because of their low cost and operational aspects related to the use of the analyzed structure. In this study, we used the genetic algorithm (GA) for detecting and locating damage. The finite element was used for diagnostic beams. Different structures considered may incur damage to be modelled by a loss of rigidity supposed to represent a defect in the structure element. Identification of damage is formulated as an optimization problem using three objective functions (change of natural frequencies, Modal Assurance Criterion MAC and MAC natural frequency). The results show that the best objective function is based on the natural frequency and MAC while the method of the genetic algorithm present its efficiencies in indicating and quantifying multiple damage with great accuracy. Three defects have been created to enhance damage depending on the elements 2, 5 and 8 with a percentage allocation of 50% in the beam structure which has been discretized into 10 elements. Finally the defect with noise was introduced to test the stability of the method against uncertainty.