三峡大学学报（自然科学版）2025，Vol.47Issue(4)：45-52,8.DOI:10.13393/j.cnki.issn.1672-948X.2025.04.007
基于图像四叉树SBFEM和深度学习的缺陷反演模型
Flaw Detection Model Based on Image Quadtree SBFEM and Deep Learning
摘要
Abstract
To quickly identify unknown internal structural flaws,a flaw detection model is proposed based on the image quadtree scaled boundary finite element method(SBFEM)and deep learning.The structural domain is meshed using the balanced quadtree algorithm with the recursive decomposition principle,and the mesh refinement processes are conducted automatically in the flaw boundary regions.The balanced quadtree mesh model is numerically analyzed using SBFEM,which ensures the solution accuracy and does not affect by hanging nodes.The model contains only six types of elements,resulting in a high degree of automation and significantly reducing the cost of the training dataset.A deep learning artificial neural network model for flaw detection is built using the extreme learning machine(ELM)as the learning rule,effectively avoiding the problem of the objective function getting trapped in local optima during model training.Statistical methods are used to analyze the accuracy of the inversion model,and the effects of flaw size and the number of training samples on the results are investigated.The results show that the predicted flaw parameters closely match the reserved clean data,and the model accurately quantifies the location and size of the flaws.关键词
缺陷反演/深度学习/图像四叉树/比例边界有限元法Key words
flaw detection/deep learning/image quadtree/scaled boundary finite element method引用本文复制引用
方涛,郑辉,赵文虎,唐森..基于图像四叉树SBFEM和深度学习的缺陷反演模型[J].三峡大学学报（自然科学版）,2025,47(4):45-52,8.基金项目
国家自然科学基金项目(52109152) （52109152）
江西省自然科学基金项目(20242BAB25023,20232BAB214086) （20242BAB25023,20232BAB214086）
江西省大学生创新创业训练计划项目(S202310403051) （S202310403051）
