Loading rate and mineralogical controls on tensile strength of rocks: a machine learning view

Machine learning models show the effects of loading rate and mineralogical composition on rock tensile strength. Difference between the indirect and direct tensile strengths becomes larger with a higher loading rate. Training with dissimilar mineralogical compositions reduces prediction reliability...

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Main Authors:	Tie, Jiahao, Meng, Wenzhao, Wei, Mingdong, Wu, Wei
Other Authors:	School of Civil and Environmental Engineering
Format:	Article
Language:	English
Published:	2023
Subjects:	Engineering::Civil engineering Tensile Strength Loading Rate
Online Access:	https://hdl.handle.net/10356/170052
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Institution:	Nanyang Technological University
Language:	English

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spelling	sg-ntu-dr.10356-1700522023-08-22T08:07:21Z Loading rate and mineralogical controls on tensile strength of rocks: a machine learning view Tie, Jiahao Meng, Wenzhao Wei, Mingdong Wu, Wei School of Civil and Environmental Engineering Engineering::Civil engineering Tensile Strength Loading Rate Machine learning models show the effects of loading rate and mineralogical composition on rock tensile strength. Difference between the indirect and direct tensile strengths becomes larger with a higher loading rate. Training with dissimilar mineralogical compositions reduces prediction reliability of machine learning models. 2023-08-22T08:07:21Z 2023-08-22T08:07:21Z 2023 Journal Article Tie, J., Meng, W., Wei, M. & Wu, W. (2023). Loading rate and mineralogical controls on tensile strength of rocks: a machine learning view. Rock Mechanics and Rock Engineering, 56(8), 6119-6125. https://dx.doi.org/10.1007/s00603-023-03354-8 0723-2632 https://hdl.handle.net/10356/170052 10.1007/s00603-023-03354-8 2-s2.0-85158144138 8 56 6119 6125 en Rock Mechanics and Rock Engineering © 2023 The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature. All rights reserved.
institution	Nanyang Technological University
building	NTU Library
continent	Asia
country	Singapore Singapore
content_provider	NTU Library
collection	DR-NTU
language	English
topic	Engineering::Civil engineering Tensile Strength Loading Rate
spellingShingle	Engineering::Civil engineering Tensile Strength Loading Rate Tie, Jiahao Meng, Wenzhao Wei, Mingdong Wu, Wei Loading rate and mineralogical controls on tensile strength of rocks: a machine learning view
description	Machine learning models show the effects of loading rate and mineralogical composition on rock tensile strength. Difference between the indirect and direct tensile strengths becomes larger with a higher loading rate. Training with dissimilar mineralogical compositions reduces prediction reliability of machine learning models.
author2	School of Civil and Environmental Engineering
author_facet	School of Civil and Environmental Engineering Tie, Jiahao Meng, Wenzhao Wei, Mingdong Wu, Wei
format	Article
author	Tie, Jiahao Meng, Wenzhao Wei, Mingdong Wu, Wei
author_sort	Tie, Jiahao
title	Loading rate and mineralogical controls on tensile strength of rocks: a machine learning view
title_short	Loading rate and mineralogical controls on tensile strength of rocks: a machine learning view
title_full	Loading rate and mineralogical controls on tensile strength of rocks: a machine learning view
title_fullStr	Loading rate and mineralogical controls on tensile strength of rocks: a machine learning view
title_full_unstemmed	Loading rate and mineralogical controls on tensile strength of rocks: a machine learning view
title_sort	loading rate and mineralogical controls on tensile strength of rocks: a machine learning view
publishDate	2023
url	https://hdl.handle.net/10356/170052
_version_	1779156801818722304

Loading rate and mineralogical controls on tensile strength of rocks: a machine learning view

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