Identification of Crack Tip Positions Based on the Scaled Boundary Finite Element Method and the Grey Wolf Optimization Algorithm

YU Bo; SUN Wenjian

doi:10.21656/1000-0887.410381

Volume 42 Issue 11

Nov. 2021

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YU Bo, SUN Wenjian. Identification of Crack Tip Positions Based on the Scaled Boundary Finite Element Method and the Grey Wolf Optimization Algorithm[J]. Applied Mathematics and Mechanics, 2021, 42(11): 1177-1189. doi: 10.21656/1000-0887.410381

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Identification of Crack Tip Positions Based on the Scaled Boundary Finite Element Method and the Grey Wolf Optimization Algorithm

doi: 10.21656/1000-0887.410381

YU Bo,
SUN Wenjian

College of Civil Engineering, Hefei University of Technology, Hefei 230009, P.R.China

Received Date: 2020-12-14
Rev Recd Date: 2021-05-06

Available Online: 2021-12-07

Publish Date: 2021-11-30

Abstract

Abstract

Based on the scaled boundary finite element method (SBFEM) and the grey wolf optimization algorithm (GWO), an identification method for crack tips was proposed. Firstly, the special advantages of the SBFEM were used to solve the fracture mechanics problem, the displacements of measurement points required in the inversion process were quickly and accurately calculated, and the correctness of the solution to the forward problem was verified in advance. Then, the objective function related to the crack tip position was established, and the identification of the crack tip position was converted to the optimization problem of solving the minimum value of the objective function. Finally, the GWO was used to optimize the objective function, that is, to search for the optimal position of the crack tip. The numerical example results show that, it is very effective to solve forward problems in the inversion process with the high precision and semi-analytical advantages of the SBFEM. The grey wolf optimization algorithm has good global convergence property, and can search for the crack tip position more quickly and accurately compared with the classical particle swarm optimization. The grey wolf optimization algorithm has good noise resistance.
- crack tip identification,
- scaled boundary finite element method,
- grey wolf optimization algorithm,
- particle swarm optimization algorithm

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References(33)

References

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