Consistent and coherent treatment of uncertainties and dependencies in fatigue crack growth calculations using multi-level Bayesian models. (December 2020)

• Record Type: Journal Article
• Authors: Francesco, Domenic Di; Chryssanthopoulos, Marios; Faber, Michael Havbro; Bharadwaj, Ujjwal
• Is Part Of: Reliability engineering & system safety. Volume 204(2020)
• Publication Date: 2020-12
• Subjects: Fatigue -- Crack growth rate -- BS 7910 -- Probabilistic model -- Bayesian data analysis -- Multi-level model -- Hierarchical model -- Model uncertainty -- Model evaluation; 00-01 -- 99-00; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2020.107117
• Languages: English
• ISSNs: 0951-8320

Abstract

Abstract : highlights: Bayesian analysis of fatigue crack growth data of British Standard, BS 7910. Hierarchical model structure for partial pooling between test datasets. Out of sample predictive performance comparisons of various model structures. Bayesian models performed better than recommendations in BS 7910 Annex K. Abstract: Engineers perform fatigue assessments to support structural integrity management. Given that the purpose of these calculations is linked to problems of decision making under various sources of uncertainty, probabilistic methods are often more useful than deterministic alternatives. Guidance on the direct probabilistic application of procedures in existing industrial standards is currently limited and dependencies between marginal probabilistic models are generally not considered, despite their potential significance being acknowledged. This paper proposes the use of Bayesian data analysis as a flexible and intuitive approach to coherently and consistently account for uncertainty and dependency in fatigue crack growth rate models. Various Bayesian models are established and presented, based on the same data as the existing models in BS 7910 (a widely used industrial standard). The models are compared in terms of their out of sample predictive accuracy, using methods with a basis in information theory and cross-validation. The Bayesian models exhibit an improved performance, with the most accurate predictions resulting from multi-level (hierarchical) models, which account for variation between constituent test datasets and partially pool information.