Modelling fatigue uncertainty by means of nonconstant variance neural networks
<p dir="ltr">The modelling of fatigue using machine learning (ML) has been gaining traction in the engineering community. Among ML techniques, the use of probabilistic neural networks (PNNs) has recently emerged as a candidate for modelling fatigue applications. In this paper, we use...
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| مؤلفون آخرون: | , |
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2022
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إضافة وسم
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| _version_ | 1864513548263096320 |
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| author | Mohamad Shadi Nashed (21363557) |
| author2 | Jamil Renno (14070771) M. Shadi Mohamed (18810406) |
| author2_role | author author |
| author_facet | Mohamad Shadi Nashed (21363557) Jamil Renno (14070771) M. Shadi Mohamed (18810406) |
| author_role | author |
| dc.creator.none.fl_str_mv | Mohamad Shadi Nashed (21363557) Jamil Renno (14070771) M. Shadi Mohamed (18810406) |
| dc.date.none.fl_str_mv | 2022-06-12T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1111/ffe.13759 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Modelling_fatigue_uncertainty_by_means_of_nonconstant_variance_neural_networks/29069573 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Mechanical engineering Information and computing sciences Artificial intelligence Machine learning Fatigue modeling Machine learning (ML) Probabilistic neural networks (PNNs) Nonconstant variance Structural health monitoring Fatigue life prediction |
| dc.title.none.fl_str_mv | Modelling fatigue uncertainty by means of nonconstant variance neural networks |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">The modelling of fatigue using machine learning (ML) has been gaining traction in the engineering community. Among ML techniques, the use of probabilistic neural networks (PNNs) has recently emerged as a candidate for modelling fatigue applications. In this paper, we use PNNs with nonconstant variance to model fatigue. We present two case studies to demonstrate the developed approach. First, we model the fatigue life of cover‐plated beams under constant amplitude loading, and then we model the relationship between random vibration velocity and equivalent stress in process pipework. The two case studies demonstrate that PNNs with nonconstant variance can model the distribution of the data while also considering the variability of both distribution parameters (mean and standard deviation). This shows the potential of PNNs with nonconstant variance in modelling fatigue applications. All the data and code used in this paper are openly available.</p><h2>Other Information</h2><p dir="ltr">Published in: Fatigue & Fracture of Engineering Materials & Structures<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1111/ffe.13759" target="_blank">https://dx.doi.org/10.1111/ffe.13759</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_e5a6a57882d2164deb362c26caef5830 |
| identifier_str_mv | 10.1111/ffe.13759 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29069573 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Modelling fatigue uncertainty by means of nonconstant variance neural networksMohamad Shadi Nashed (21363557)Jamil Renno (14070771)M. Shadi Mohamed (18810406)EngineeringMechanical engineeringInformation and computing sciencesArtificial intelligenceMachine learningFatigue modelingMachine learning (ML)Probabilistic neural networks (PNNs)Nonconstant varianceStructural health monitoringFatigue life prediction<p dir="ltr">The modelling of fatigue using machine learning (ML) has been gaining traction in the engineering community. Among ML techniques, the use of probabilistic neural networks (PNNs) has recently emerged as a candidate for modelling fatigue applications. In this paper, we use PNNs with nonconstant variance to model fatigue. We present two case studies to demonstrate the developed approach. First, we model the fatigue life of cover‐plated beams under constant amplitude loading, and then we model the relationship between random vibration velocity and equivalent stress in process pipework. The two case studies demonstrate that PNNs with nonconstant variance can model the distribution of the data while also considering the variability of both distribution parameters (mean and standard deviation). This shows the potential of PNNs with nonconstant variance in modelling fatigue applications. All the data and code used in this paper are openly available.</p><h2>Other Information</h2><p dir="ltr">Published in: Fatigue & Fracture of Engineering Materials & Structures<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1111/ffe.13759" target="_blank">https://dx.doi.org/10.1111/ffe.13759</a></p>2022-06-12T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1111/ffe.13759https://figshare.com/articles/journal_contribution/Modelling_fatigue_uncertainty_by_means_of_nonconstant_variance_neural_networks/29069573CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/290695732022-06-12T09:00:00Z |
| spellingShingle | Modelling fatigue uncertainty by means of nonconstant variance neural networks Mohamad Shadi Nashed (21363557) Engineering Mechanical engineering Information and computing sciences Artificial intelligence Machine learning Fatigue modeling Machine learning (ML) Probabilistic neural networks (PNNs) Nonconstant variance Structural health monitoring Fatigue life prediction |
| status_str | publishedVersion |
| title | Modelling fatigue uncertainty by means of nonconstant variance neural networks |
| title_full | Modelling fatigue uncertainty by means of nonconstant variance neural networks |
| title_fullStr | Modelling fatigue uncertainty by means of nonconstant variance neural networks |
| title_full_unstemmed | Modelling fatigue uncertainty by means of nonconstant variance neural networks |
| title_short | Modelling fatigue uncertainty by means of nonconstant variance neural networks |
| title_sort | Modelling fatigue uncertainty by means of nonconstant variance neural networks |
| topic | Engineering Mechanical engineering Information and computing sciences Artificial intelligence Machine learning Fatigue modeling Machine learning (ML) Probabilistic neural networks (PNNs) Nonconstant variance Structural health monitoring Fatigue life prediction |