State-of-the-art ensemble learning and unsupervised learning in fatigue crack recognition of glass fiber reinforced polyester composite (GFRP) using acoustic emission
Fatigue strength is one of the most important properties of composite materials because it directly relates to their lifespan. Acoustic emission (AE) is a passive structural health monitoring (SHM) technique that provides real-time damage detection based on stress waves generated by cracks in the st...
| Main Authors: | , , |
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| Format: | Article |
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Elsevier
2023
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| Online Access: | http://psasir.upm.edu.my/id/eprint/109502/ |
| _version_ | 1848865388114214912 |
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| author | Gholizadeh, S. Leman, Z. Baharudin, B.T.H.T. |
| author_facet | Gholizadeh, S. Leman, Z. Baharudin, B.T.H.T. |
| author_sort | Gholizadeh, S. |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Fatigue strength is one of the most important properties of composite materials because it directly relates to their lifespan. Acoustic emission (AE) is a passive structural health monitoring (SHM) technique that provides real-time damage detection based on stress waves generated by cracks in the structure. This study evaluates the damage progression on glass fiber reinforced polyester composite specimens using different approaches of machine learning. Different methodologies for damage detection and characterization of AE parameters are presented. Three different ensemble learning methods namely, XGboost, LightGBM, and CatBoost were chosen to predict damages and AE parameters. SHAP values were used to select AE key features and K-means algorithms were employed to classify damage severity. The accuracy of these approaches demonstrates the reliability of various machine learning techniques in predicting the fatigue life of composite materials using acoustic emission. |
| first_indexed | 2025-11-15T14:03:55Z |
| format | Article |
| id | upm-109502 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T14:03:55Z |
| publishDate | 2023 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1095022024-11-06T02:26:20Z http://psasir.upm.edu.my/id/eprint/109502/ State-of-the-art ensemble learning and unsupervised learning in fatigue crack recognition of glass fiber reinforced polyester composite (GFRP) using acoustic emission Gholizadeh, S. Leman, Z. Baharudin, B.T.H.T. Fatigue strength is one of the most important properties of composite materials because it directly relates to their lifespan. Acoustic emission (AE) is a passive structural health monitoring (SHM) technique that provides real-time damage detection based on stress waves generated by cracks in the structure. This study evaluates the damage progression on glass fiber reinforced polyester composite specimens using different approaches of machine learning. Different methodologies for damage detection and characterization of AE parameters are presented. Three different ensemble learning methods namely, XGboost, LightGBM, and CatBoost were chosen to predict damages and AE parameters. SHAP values were used to select AE key features and K-means algorithms were employed to classify damage severity. The accuracy of these approaches demonstrates the reliability of various machine learning techniques in predicting the fatigue life of composite materials using acoustic emission. Elsevier 2023-03-25 Article PeerReviewed Gholizadeh, S. and Leman, Z. and Baharudin, B.T.H.T. (2023) State-of-the-art ensemble learning and unsupervised learning in fatigue crack recognition of glass fiber reinforced polyester composite (GFRP) using acoustic emission. Ultrasonics, 132. pp. 1-12. ISSN 0041-624X; eISSN: 1874-9968 https://linkinghub.elsevier.com/retrieve/pii/S0041624X23000744 10.1016/j.ultras.2023.106998 |
| spellingShingle | Gholizadeh, S. Leman, Z. Baharudin, B.T.H.T. State-of-the-art ensemble learning and unsupervised learning in fatigue crack recognition of glass fiber reinforced polyester composite (GFRP) using acoustic emission |
| title | State-of-the-art ensemble learning and unsupervised learning in fatigue crack recognition of glass fiber reinforced polyester composite (GFRP) using acoustic emission |
| title_full | State-of-the-art ensemble learning and unsupervised learning in fatigue crack recognition of glass fiber reinforced polyester composite (GFRP) using acoustic emission |
| title_fullStr | State-of-the-art ensemble learning and unsupervised learning in fatigue crack recognition of glass fiber reinforced polyester composite (GFRP) using acoustic emission |
| title_full_unstemmed | State-of-the-art ensemble learning and unsupervised learning in fatigue crack recognition of glass fiber reinforced polyester composite (GFRP) using acoustic emission |
| title_short | State-of-the-art ensemble learning and unsupervised learning in fatigue crack recognition of glass fiber reinforced polyester composite (GFRP) using acoustic emission |
| title_sort | state-of-the-art ensemble learning and unsupervised learning in fatigue crack recognition of glass fiber reinforced polyester composite (gfrp) using acoustic emission |
| url | http://psasir.upm.edu.my/id/eprint/109502/ http://psasir.upm.edu.my/id/eprint/109502/ http://psasir.upm.edu.my/id/eprint/109502/ |