Experimental and Machine Learning Models for Stress Amplitude Prediction in Damaged GFRP Composite PipeSource: Journal of Engineering Mechanics:;2026:;Volume ( 152 ):;issue: 002::page 04025099-1Author:Brahim, Abdelmoumin Oulad
,
Capozucca, Roberto
,
Fantuzzi, Nicholas
,
Khatir, Samir
,
Cuong-Le, Thanh
DOI: 10.1061/JENMDT.EMENG-8663Publisher: American Society of Civil Engineers
Abstract: AbstractIn this paper, a comprehensive modal analysis is conducted, utilizing experimental
and numerical approaches to investigate and predict the influence of the damage size
on structural behavior. A finite element (FE) model is created from different ...
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| contributor author | Brahim, Abdelmoumin Oulad | |
| contributor author | Capozucca, Roberto | |
| contributor author | Fantuzzi, Nicholas | |
| contributor author | Khatir, Samir | |
| contributor author | Cuong-Le, Thanh | |
| date accessioned | 2026-08-20T10:51:16Z | |
| date available | 2026-08-20T10:51:16Z | |
| date copyright | 2025/12/09 | |
| date issued | 2026 | |
| identifier other | JENMDT.EMENG-8663.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4311354 | |
| description abstract | AbstractIn this paper, a comprehensive modal analysis is conducted, utilizing experimental and numerical approaches to investigate and predict the influence of the damage size on structural behavior. A finite element (FE) model is created from different ... | |
| publisher | American Society of Civil Engineers | |
| title | Experimental and Machine Learning Models for Stress Amplitude Prediction in Damaged GFRP Composite Pipe | |
| type | Journal Article | |
| journal volume | 152 | |
| journal issue | 2 | |
| journal title | Journal of Engineering Mechanics | |
| identifier doi | 10.1061/JENMDT.EMENG-8663 | |
| journal fristpage | 04025099-1 | |
| journal lastpage | 04025099-15 | |
| page | 15 | |
| tree | Journal of Engineering Mechanics:;2026:;Volume ( 152 ):;issue: 002 | |
| contenttype | Fulltext |