Fatigue of tubular joints and fatigue improvement methods
β Scribed by Professor P J Haagensen
- Publisher
- John Wiley and Sons
- Year
- 1997
- Tongue
- English
- Weight
- 979 KB
- Volume
- 1
- Category
- Article
- ISSN
- 1365-0556
No coin nor oath required. For personal study only.
β¦ Synopsis
Abstract
This paper reviews important parameters and conditions that determine the life of welded joints, and summarizes recent research developments in the fatigue design of welded structures, with an emphasis on offshore tubular structures. Two of the factors that strongly affect fatigue life, the environment and plate thickness, have been the subject of several large research programs in recent years. With the increasing use of higher strength steels much interest has also been taken in various methods for improving the fatigue life of welded joints. Developments in these areas of research are reviewed. Examples of approaches to fatigue life prediction in design codes are given. Recent changes to design codes are evaluated, and examples of significant changes in design codes are given. Progress in the implementation of techniques for fatigue life improvement and life extension of fatigueβdamaged structures are discussed.
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## Abstract Tubular space trusses for bridge applications use thickβwalled tubes. The reduction in fatigue resistance due to geometrical size effects is thus an important issue. In order to carry out a thorough study, both fatigue tests on largeβscale specimens and advanced 3D crack propagation mod
## Abstract Only for steels up to grade S690QL the wellβknown fact is confirmed that the fatigue strength of welded joints is independent of the material. For higher strength steels a remarkable reduction of the fatigue strength is found. Some advice is given for designers in which situations the
In the past, several methods have been proposed to predict fatigue crack growth rate in tubular joints of offshore structures, however reasonably accurate solution for this problem is still lacking. Dramatic increase in the use of neural neural networks (NN) in material science, specially fatigue ar