Imperial College London
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DrVitoTagarielli

Faculty of Engineering, Department of Aeronautics

Reader in Mechanics of Solids
 
 
 
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Contact

 

+44 (0)20 7594 5167v.tagarielli

 
 
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Location

 

218City and Guilds BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Chavoshi:2023:10.1111/ffe.13858,
author = {Chavoshi, SZ and Tagarielli, VL},
doi = {10.1111/ffe.13858},
journal = {Fatigue and Fracture of Engineering Materials and Structures},
pages = {212--227},
title = {Data-driven prediction of the probability of creep-fatigue crack initiation in 316H stainless steel},
url = {http://dx.doi.org/10.1111/ffe.13858},
volume = {46},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Stainless steel components in advanced gas-cooled reactors (AGRs) are susceptible to creep–fatigue cracking at high temperatures. Quantifying the probability of creep–fatigue crack initiation requires probabilistic numerical simulations; these are complex and computationally intensive. Here, we present a data-driven approach to develop fast probabilistic surrogate models of creep–fatigue crack initiation in 316H stainless steel. We perform a set of Monte Carlo simulations based on the R5V2/3 high temperature assessment procedure and determine the sensitivity of the probability of crack initiation to loads and operating conditions. The data are used to train different supervised machine learning models considering Bayesian hyperparameter optimization. We discuss the relative performance of such models and show that a gradient tree boosting algorithm results in surrogate models with the highest accuracy.
AU  - Chavoshi,SZ
AU  - Tagarielli,VL
DO  - 10.1111/ffe.13858
EP  - 227
PY  - 2023///
SN  - 1460-2695
SP  - 212
TI  - Data-driven prediction of the probability of creep-fatigue crack initiation in 316H stainless steel
T2  - Fatigue and Fracture of Engineering Materials and Structures
UR  - http://dx.doi.org/10.1111/ffe.13858
UR  - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000869309700001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://onlinelibrary.wiley.com/doi/10.1111/ffe.13858
UR  - http://hdl.handle.net/10044/1/100677
VL  - 46
ER  -
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