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Assessing the predictive performance of creep models using absolute rather than squared prediction errors: an application to 2.25Cr-1Mo steel and 316H stainless steel

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Materials at High Temperatures, Pages: 1 - 12

Swansea University Author: Mark Evans Orcid Logo

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DOI (Published version): 10.1080/09603409.2023.2268332

Abstract

A reliable means of assessing the accuracy of a creep model’s predictions is fundamental to safe power plant operation. This paper introduces a method of decomposing the mean absolute prediction error for such a purpose to overcome the limitations that are inherent in the traditional approach of squ...

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Published in: Materials at High Temperatures
ISSN: 0960-3409 1878-6413
Published: Informa UK Limited 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa64687
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first_indexed 2023-10-10T10:01:49Z
last_indexed 2023-10-10T10:01:49Z
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spelling v2 64687 2023-10-10 Assessing the predictive performance of creep models using absolute rather than squared prediction errors: an application to 2.25Cr-1Mo steel and 316H stainless steel 7720f04c308cf7a1c32312058780d20c 0000-0003-2056-2396 Mark Evans Mark Evans true false 2023-10-10 MTLS A reliable means of assessing the accuracy of a creep model’s predictions is fundamental to safe power plant operation. This paper introduces a method of decomposing the mean absolute prediction error for such a purpose to overcome the limitations that are inherent in the traditional approach of squaring prediction errors to prevent over and underestimates of life offsetting each other. When this method is applied to 2.25Cr-1Mo steel and 316 H stainless steel, it was found that squared errors leads to overestimates of the average prediction error associated with a particular creep model, and it also dramatically underestimates the proportion of this error that is systematic in nature. These differences were more noticeable for 316 H stainless steel. Journal Article Materials at High Temperatures 1 12 Informa UK Limited 0960-3409 1878-6413 Mean percentage squared error, mean percentage absolute error, error decomposition, parametric creep models, life assessment 16 10 2023 2023-10-16 10.1080/09603409.2023.2268332 http://dx.doi.org/10.1080/09603409.2023.2268332 COLLEGE NANME Materials Science and Engineering COLLEGE CODE MTLS Swansea University SU Library paid the OA fee (TA Institutional Deal) Swansea University 2023-11-17T14:06:58.8592679 2023-10-10T10:59:37.1407705 Faculty of Science and Engineering School of Engineering and Applied Sciences - Materials Science and Engineering Mark Evans 0000-0003-2056-2396 1 64687__28863__c073dad7bbb34f14ba97a7e9f04e1803.pdf 64687 (2).pdf 2023-10-25T09:05:19.8573845 Output 1497262 application/pdf Version of Record true © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. Distributed under the terms of a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). true eng http://creativecommons.org/licenses/by-nc-nd/4.0/
title Assessing the predictive performance of creep models using absolute rather than squared prediction errors: an application to 2.25Cr-1Mo steel and 316H stainless steel
spellingShingle Assessing the predictive performance of creep models using absolute rather than squared prediction errors: an application to 2.25Cr-1Mo steel and 316H stainless steel
Mark Evans
title_short Assessing the predictive performance of creep models using absolute rather than squared prediction errors: an application to 2.25Cr-1Mo steel and 316H stainless steel
title_full Assessing the predictive performance of creep models using absolute rather than squared prediction errors: an application to 2.25Cr-1Mo steel and 316H stainless steel
title_fullStr Assessing the predictive performance of creep models using absolute rather than squared prediction errors: an application to 2.25Cr-1Mo steel and 316H stainless steel
title_full_unstemmed Assessing the predictive performance of creep models using absolute rather than squared prediction errors: an application to 2.25Cr-1Mo steel and 316H stainless steel
title_sort Assessing the predictive performance of creep models using absolute rather than squared prediction errors: an application to 2.25Cr-1Mo steel and 316H stainless steel
author_id_str_mv 7720f04c308cf7a1c32312058780d20c
author_id_fullname_str_mv 7720f04c308cf7a1c32312058780d20c_***_Mark Evans
author Mark Evans
author2 Mark Evans
format Journal article
container_title Materials at High Temperatures
container_start_page 1
publishDate 2023
institution Swansea University
issn 0960-3409
1878-6413
doi_str_mv 10.1080/09603409.2023.2268332
publisher Informa UK Limited
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Materials Science and Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Materials Science and Engineering
url http://dx.doi.org/10.1080/09603409.2023.2268332
document_store_str 1
active_str 0
description A reliable means of assessing the accuracy of a creep model’s predictions is fundamental to safe power plant operation. This paper introduces a method of decomposing the mean absolute prediction error for such a purpose to overcome the limitations that are inherent in the traditional approach of squaring prediction errors to prevent over and underestimates of life offsetting each other. When this method is applied to 2.25Cr-1Mo steel and 316 H stainless steel, it was found that squared errors leads to overestimates of the average prediction error associated with a particular creep model, and it also dramatically underestimates the proportion of this error that is systematic in nature. These differences were more noticeable for 316 H stainless steel.
published_date 2023-10-16T14:07:01Z
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score 11.013082

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