A review of experimentation and computational modeling of dynamic bolt fracture. (July 2022)

• Record Type: Journal Article
• Authors: Warren, Maria; Antoniou, Antonia; Stewart, Lauren
• Is Part Of: Journal of constructional steel research. Volume 194(2022)
• Publication Date: 2022-07
• Subjects: Structural connections -- Impulsive loading -- Progressive collapse -- Fracture mechanics; Steel, Structural -- Periodicals; Building, Iron and steel -- Periodicals; Acier de construction -- Périodiques; Construction métallique -- Périodiques; 624.1821
• DOI: 10.1016/j.jcsr.2022.107293
• Languages: English
• ISSNs: 0143-974X

Abstract

Abstract: Structures that undergo impulsive loading, such as from a blast or impact, are at risk of damage and progressive collapse. In these cases, the ductile response of structural connections is vital to preventing widespread failures and maintaining structural integrity. Ductile connections undergo significant plastic deformation, which dissipates energy from the impulse and facilitates safe evacuation of damaged structures. Alarmingly, in situ reports and experimental results have shown that fasteners under dynamic loading may fail in a brittle manner, however, this has not been investigated extensively. This review presents the current state-of-the-art of experimental and numerical work towards prediction of dynamic bolt behavior. There has been recent success in modeling bolt failure using fracture mechanics principles, but this is limited by gaps in knowledge of the dynamic material characteristics and microstructure of structural bolts. This review highlights key research topics necessary to understand mechanisms of bolt dynamic fracture and establishment of failure criteria for modeling. Highlights: Bolts in structural connections subjected to impulsive loading in situ exhibit ductility loss, compromising structural safety Prediction of bolt fracture may benefit from a fracture mechanics-based modeling approach Structural bolt flaws, microstructure, and material properties must be characterized to improve computational modeling