Functional Weibull-based models of steel fracture toughness for structural risk analysis: estimation and selection. (September 2017)

• Record Type: Journal Article
• Authors: Pérot, Nadia; Bousquet, Nicolas
• Is Part Of: Reliability engineering & system safety. Volume 165(2017)
• Page Start: 355
• Page End: 367
• Publication Date: 2017-09
• Subjects: Weibull -- Master Curve -- Censoring -- Genetic algorithm -- Thickness correction -- Model selection -- Fracture toughness -- Reliability; 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.2017.04.024
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: A complete methodology for assessing models for steel fracture toughness that extend the Master Curve. Solves incoherencies in estimation of former approach. Uses information yielded by both standard and non-standard data. Specific approaches of model selection. Provides a refinement for the modelling of a major influent random variable in structural reliability analysis. Abstract: A key input component of numerous reliability studies of industrial components or structures, steel fracture toughness is usually considered as a random process because of its natural variability. Moreover, toughness presents a high sensitivity to temperature which also plays a fundamental role, as an environmental forcing, in such studies. Therefore a particular attention has to be paid to the assessment of its stochastic functional modelling, conducted by a statistical analysis of indirect measures. While a Weibull shape arising from statistical physics is recognized as one of the most relevant approach to represent local variability, the selection of functional parameters requires an accurate methodology of fracture toughness modelling. This article provides such a methodology, that solves inconsistencies in former data treatments. The innovation consists in three improvements: (a) the thickness correction of the steel specimen is included throughout the calculation and not performed a priori; (b) nonstandard but informative data are included in the assessment as censored data; (c) a chi-square test is developed to assess the model quality relatively to fracture toughness data, indexed by temperature. Illustrated by the exploration of a database feed by several European manufacturers, this complete methodology is implemented in a dedicated software tool.