Adaptive Gaussian process regression as an alternative to FEM for prediction of stress intensity factor to assess fatigue degradation in offshore pipeline-Reference-Cited by-同舟云学术

Adaptive Gaussian process regression as an alternative to FEM for prediction of stress intensity factor to assess fatigue degradation in offshore pipeline

Published:2017-06 Issue: Volume:153 Page:45-58
ISSN:0308-0161
Container-title:International Journal of Pressure Vessels and Piping
language:en
Short-container-title:International Journal of Pressure Vessels and Piping

Author:

Keprate Arvind^ORCID,Ratnayake R.M. Chandima,Sankararaman Shankar

Funder

Norwegian Ministry of Education

NASA Prognostics Center of Excellence

Publisher

Elsevier BV

Subject

Mechanical Engineering,Mechanics of Materials,General Materials Science

Reference48 articles.

1. Enhancing offshore process safety by selecting fatigue critical pipeline locations for inspection using Fuzzy-AHP based approach;Keprate;Process Saf Environ,2017

2. Guidelines for the avoidance of vibration induced fatigue failure in process pipework;Guidelines,2007

3. Fatigue design of offshore steel structures;DNVGL-RP-C203,2007

4. Probabilistic methods for planning of inspection for fatigue cracks in offshore structures;DNVGL-RP-C210,2015

5. Guide to methods for assessing the acceptability of flaws in metallic structures;BSI.BS 7910,2013

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