journal article·2020-01

Engineering critical assessment and variable sensitivity analysis for as-welded S690 steels

Y Wang, A MacDonald, L Xu, M Wright, RA Shenoi

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Abstract

Abstract Engineering critical assessment (ECA) or fitness-for-service (FFS) is a fracture mechanics based approach that is increasingly utilised for assessing the structural integrity of welded steel structures considering various degradation mechanisms which may occur during service. However, if sufficient number of inputs and/or accurate input values are not available, the ECA result is often considered overly conservative and for such cases adds potentially unnecessary obstacles to design and maintenance solutions High strength steels such as S690, S890 and S960 are increasingly used in the offshore and lifting applications, thus to enhance our confidence in using such steels, it is essential to develop better understanding into the relationship between the information from ECA, fracture toughness that can be currently achieved in industry and the required toughness from rules and regulations. This study explores the influence of different variables on critical fracture toughness requirement of S690 steel structures. In addition, the correlation between all the studied variables are investigated to provide quantitative indications of the most important variables in a static ECA process for high strength steel applications. Quantified sensitivity and correlations are obtained for the six studied variables. Industrial fracture toughness data of welded S690 with section thickness up to 160 mm are collected. This provides a snapshot of currently achievable fracture toughens of S690, in comparison with the ECA results and current rules and standards.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Fatigue and fracture mechanics · Hydrogen embrittlement and corrosion behaviors in metals · Material Properties and Failure Mechanisms

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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