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Improving the performance of damage repair in thin-walled structures with analytical data and machine learning algorithms

In the last four decades, bonded composite repair has proven to be an effective method for addressing crack damage propagation. On the other hand, machine learning (ML) has made it possible to employ a variety of approaches for mechanical and aerospace problems and such significant approach is the r...

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Main Authors: Shaikh, Abdul Aabid, Raheman, Md Abdul, Hrairi, Meftah, Baig, Muneer
Format: Article
Language:English
English
Published: Gruppo Italiano Frattura 2024
Subjects:
TA349 Mechanics of engineering. Applied mechanics
TA630 Structural engineering (General)
TJ212 Control engineering
Online Access:http://irep.iium.edu.my/111676/7/111676_Improving%20the%20performance%20of%20damage.pdf
http://irep.iium.edu.my/111676/8/111676_Improving%20the%20performance%20of%20damage_Scopus.pdf
http://irep.iium.edu.my/111676/
https://www.fracturae.com/index.php/fis/article/view/4838
https://doi.org/10.3221/IGF-ESIS.68.21
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spelling my.iium.irep.1116762024-04-01T07:10:42Z http://irep.iium.edu.my/111676/ Improving the performance of damage repair in thin-walled structures with analytical data and machine learning algorithms Shaikh, Abdul Aabid Raheman, Md Abdul Hrairi, Meftah Baig, Muneer TA349 Mechanics of engineering. Applied mechanics TA630 Structural engineering (General) TJ212 Control engineering In the last four decades, bonded composite repair has proven to be an effective method for addressing crack damage propagation. On the other hand, machine learning (ML) has made it possible to employ a variety of approaches for mechanical and aerospace problems and such significant approach is the repair mechanism and hence ML algorithms used to enhance in the present work. The current work investigates the effect of the single-sided composite patch bonded on a thin plate under plane stress conditions. An analytical model was formulated for a single-sided composite patch repair using linear elastic fracture mechanics and Rose's analytical modelling. From the analytical model, the stress intensity factors (SIF) were calculated by varying all possible parameters of the model. Next, ML algorithms were selected, and comparative studies were conducted for the best possible performance and to identify the parametric effects on optimum SIF. Also, the analytical model is validated with existing work, and it shows good agreement with less than 10% error. This study is particularly important for designing the single-sided composite patch repair method based on analytical modelling. Also, it is important to compare ML algorithms with analytical solutions in regression applications. Gruppo Italiano Frattura 2024-03-05 Article PeerReviewed application/pdf en http://irep.iium.edu.my/111676/7/111676_Improving%20the%20performance%20of%20damage.pdf application/pdf en http://irep.iium.edu.my/111676/8/111676_Improving%20the%20performance%20of%20damage_Scopus.pdf Shaikh, Abdul Aabid and Raheman, Md Abdul and Hrairi, Meftah and Baig, Muneer (2024) Improving the performance of damage repair in thin-walled structures with analytical data and machine learning algorithms. Frattura ed Integrita Strutturale, 18 (68). pp. 310-324. E-ISSN 1971-8993 https://www.fracturae.com/index.php/fis/article/view/4838 https://doi.org/10.3221/IGF-ESIS.68.21
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
English
topic TA349 Mechanics of engineering. Applied mechanics
TA630 Structural engineering (General)
TJ212 Control engineering
spellingShingle TA349 Mechanics of engineering. Applied mechanics
TA630 Structural engineering (General)
TJ212 Control engineering
Shaikh, Abdul Aabid
Raheman, Md Abdul
Hrairi, Meftah
Baig, Muneer
Improving the performance of damage repair in thin-walled structures with analytical data and machine learning algorithms
description In the last four decades, bonded composite repair has proven to be an effective method for addressing crack damage propagation. On the other hand, machine learning (ML) has made it possible to employ a variety of approaches for mechanical and aerospace problems and such significant approach is the repair mechanism and hence ML algorithms used to enhance in the present work. The current work investigates the effect of the single-sided composite patch bonded on a thin plate under plane stress conditions. An analytical model was formulated for a single-sided composite patch repair using linear elastic fracture mechanics and Rose's analytical modelling. From the analytical model, the stress intensity factors (SIF) were calculated by varying all possible parameters of the model. Next, ML algorithms were selected, and comparative studies were conducted for the best possible performance and to identify the parametric effects on optimum SIF. Also, the analytical model is validated with existing work, and it shows good agreement with less than 10% error. This study is particularly important for designing the single-sided composite patch repair method based on analytical modelling. Also, it is important to compare ML algorithms with analytical solutions in regression applications.
format Article
author Shaikh, Abdul Aabid
Raheman, Md Abdul
Hrairi, Meftah
Baig, Muneer
author_facet Shaikh, Abdul Aabid
Raheman, Md Abdul
Hrairi, Meftah
Baig, Muneer
author_sort Shaikh, Abdul Aabid
title Improving the performance of damage repair in thin-walled structures with analytical data and machine learning algorithms
title_short Improving the performance of damage repair in thin-walled structures with analytical data and machine learning algorithms
title_full Improving the performance of damage repair in thin-walled structures with analytical data and machine learning algorithms
title_fullStr Improving the performance of damage repair in thin-walled structures with analytical data and machine learning algorithms
title_full_unstemmed Improving the performance of damage repair in thin-walled structures with analytical data and machine learning algorithms
title_sort improving the performance of damage repair in thin-walled structures with analytical data and machine learning algorithms
publisher Gruppo Italiano Frattura
publishDate 2024
url http://irep.iium.edu.my/111676/7/111676_Improving%20the%20performance%20of%20damage.pdf
http://irep.iium.edu.my/111676/8/111676_Improving%20the%20performance%20of%20damage_Scopus.pdf
http://irep.iium.edu.my/111676/
https://www.fracturae.com/index.php/fis/article/view/4838
https://doi.org/10.3221/IGF-ESIS.68.21
_version_ 1797904129730805760
score 13.211869

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