A probabilistic model for quantifying uncertainty in the failure assessment diagram. (November 2022)

• Record Type: Journal Article
• Authors: Di Francesco, Domenic; Girolami, Mark; Duncan, Andrew B.; Chryssanthopoulos, Marios
• Is Part Of: Structural safety. Volume 99(2022)
• Publication Date: 2022-11
• Subjects: Data-centric engineering -- Experimental design -- Fracture mechanics -- Gaussian process regression -- Structural reliability; Structural stability -- Periodicals; Safety factor in engineering -- Periodicals; Reliability (Engineering) -- Periodicals; Constructions -- Stabilité -- Périodiques; Coefficient de sécurité en ingénierie -- Périodiques; Fiabilité -- Périodiques; 620.86
• DOI: 10.1016/j.strusafe.2022.102262
• Languages: English
• ISSNs: 0167-4730

Abstract

Abstract: Failure assessment diagrams are an integral component of asset integrity management in a variety of industrial sectors. They allow for the assessment of the significance of cracks in structures, between the domains of brittle fracture and plastic collapse. Numerical modelling, or empirically determined assessment lines across this continuum, are the basis of the guidance in current industrial standards. However, as the requirement of such assessments progresses from demonstrating safety to optimising resilience and resource allocation, it will become necessary to compute more informative (probabilistic) estimates, which are compatible with decision analysis. In this paper, Bayesian regression models are proposed as a suitable method of quantifying (aleatory and epistemic) uncertainty in the limit state on a failure assessment diagram. The data considered in this study consists of laboratory tests completed on wide plate fracture specimens. This work is intended to address the inconsistencies in current editions of industrial standards, and limitations (regarding flexibility and application) of existing scientific literature on the topic. Potential applications are discussed, including the use of fracture mechanics in meaningful reliability analysis, quantitative risk management, and optimising experimental design for future material tests. Highlights: A probabilistic failure assessment diagram is compared to methods used in industry. The proposed gaussian process model is flexible, non-linear and probabilistic. It can therefore approximate the behaviour between brittle and plastic domains. Applications of reliability analysis and experimental design are demonstrated.