An adaptive multiscale approach for identifying multiple flaws based on XFEM and a discrete artificial fish swarm algorithm

作者：Zhao, Wenhu; Du, Chengbin^*; Jiang, Shouyan^*

来源：Computer Methods in Applied Mechanics and Engineering, 2018, 339: 341-357.

DOI：10.1016/j.cma.2018.04.037

• Discrete artificial fish swarm algorithm
• Artificial bee colony algorithm
• Multiple flaws
• XFEM
• K-means
• Inverse problem

An adaptive multiscale approach for identifying multiple flaws in structures is proposed in this paper. The approach includes a two-step process in which a coarse-scale search initially identifies the candidate subdomains and then a fine-scale search of the narrow subdomains captures the true flaws. The extended finite element method (XFEM) is employed to solve the forward problem because it does not require re-meshing in each iteration. A discrete artificial fish swarm (DAFS) algorithm with adaptive vision is adopted to accelerate the convergence of the objective function. The probable subdomains are output via K-means clustering with an initial estimated number of flaws Then, the artificial bee colony (ABC) algorithm is employed to capture the true flaws and remove the false flaws. The DAFS algorithm is a single-population algorithm, while the ABC algorithm is a multi-population algorithm. Three numerical examples are studied to evaluate the accuracy and performance of the proposed approach. Circular and elliptical flaws and the effects of measurement noise are considered to test the robustness. The results show that this approach can effectively quantify and identify multiple internal flaws without any prior knowledge of their quantity. Furthermore, the strategy is more efficient than previously proposed approaches.

• 出版日期2018-9-1
• 单位河海大学