An adaptive learning approach to determine and update crack sizes from strain relaxation data for welded plate joints. (January 2022)

• Record Type: Journal Article
• Authors: Feng, Liuyang; Qian, Xudong
• Is Part Of: Engineering fracture mechanics. Volume 259(2022)
• Publication Date: 2022-01
• Subjects: Crack sizing -- Neural network -- Modified bootstrap particle filtering -- Strain relaxation -- Welded plate joints -- Digital twin; Fracture mechanics -- Periodicals; Rupture, Mécanique de la -- Périodiques; Fracture mechanics; Periodicals; 620.112605
• DOI: 10.1016/j.engfracmech.2021.108165
• Languages: English
• ISSNs: 0013-7944

Abstract

Highlights: We present a learning approach to determine and update the crack size in welded joints. The crack size determination is treated as a classification problem in neural network. The updating of the crack size utilizes modified bootstrap particle filtering method. The updated algorithm predicts accurately the growing crack size using limited measured data. Abstract: This paper proposes the framework to determine and update the crack-front profile at the toe of welded plate joints based on the strain relaxation data. This study determines the crack depth by classifying the nodes in the thickness direction as open nodes on the crack surface and closed nodes on the intact ligament. To update the crack size during the loading history, this research employs the modified bootstrap particle filtering approach, which entails enhanced adjustment capabilities by imposing additional uncertainty distributions. This approach improves the crack size prediction by absorbing limited measurement data on the strain values or crack sizes.