Experimental vibration dataset collected of a beam reinforced with masses under different health conditions
Vibration signals extracted from structures across diverse health conditions have become indispensable for monitoring structural integrity. These datasets represent a resource for real-time condition monitoring, enabling the precise detection and diagnosis of system anomalies. This paper aims to enr...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-02-01
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| Series: | Data in Brief |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340924000179 |
| _version_ | 1827353659592146944 |
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| author | Amanda A.S.R. de Sousa Marcela R. Machado |
| author_facet | Amanda A.S.R. de Sousa Marcela R. Machado |
| author_sort | Amanda A.S.R. de Sousa |
| collection | DOAJ |
| description | Vibration signals extracted from structures across diverse health conditions have become indispensable for monitoring structural integrity. These datasets represent a resource for real-time condition monitoring, enabling the precise detection and diagnosis of system anomalies. This paper aims to enrich the scientific community's database on structural dynamics and experimental methodologies pertinent to system modelling. Leveraging experimental measurements obtained from mass-reinforced beams, these datasets validate numerical models, refine identification techniques, quantify uncertainties, and continuously foster machine learning algorithms' evolution to monitor structural integrity. Furthermore, the beam dataset is data-driven and can be used to develop and test innovative structural health monitoring strategies, specifically identifying damages and anomalies within intricate structural frameworks. Supplemental datasets like Mass-position and damage index introduce parametric uncertainty into experimental and damage identification metrics. Thereby offering valuable insights to elevate the efficacy of monitoring and control techniques. These comprehensive tests also encapsulate paramedic uncertainty, providing robust support for applications in uncertainty quantification, stochastic modelling, and supervised and unsupervised machine learning methodologies. |
| first_indexed | 2024-03-08T03:30:38Z |
| format | Article |
| id | doaj.art-c219a19592764176bc1d9f38fe9851fc |
| institution | Directory Open Access Journal |
| issn | 2352-3409 |
| language | English |
| last_indexed | 2024-03-08T03:30:38Z |
| publishDate | 2024-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj.art-c219a19592764176bc1d9f38fe9851fc2024-02-11T05:11:04ZengElsevierData in Brief2352-34092024-02-0152110043Experimental vibration dataset collected of a beam reinforced with masses under different health conditionsAmanda A.S.R. de Sousa0Marcela R. Machado1Department of Mechanical Engineering, University of Brasilia, 70910-900, Brasília, BrazilDepartment of Mechanical Engineering, University of Brasilia, 70910-900, Brasília, Brazil; Faculty of Civil, Environmental Engineering and Architecture, Bydgoszcz University of Science and Technology, Sylwestra Kaliskiego 7, Bydgoszcz, Poland; Corresponding author at: Department of Mechanical Engineering, University of Brasilia, 70910-900, Brasília, Brazil.Vibration signals extracted from structures across diverse health conditions have become indispensable for monitoring structural integrity. These datasets represent a resource for real-time condition monitoring, enabling the precise detection and diagnosis of system anomalies. This paper aims to enrich the scientific community's database on structural dynamics and experimental methodologies pertinent to system modelling. Leveraging experimental measurements obtained from mass-reinforced beams, these datasets validate numerical models, refine identification techniques, quantify uncertainties, and continuously foster machine learning algorithms' evolution to monitor structural integrity. Furthermore, the beam dataset is data-driven and can be used to develop and test innovative structural health monitoring strategies, specifically identifying damages and anomalies within intricate structural frameworks. Supplemental datasets like Mass-position and damage index introduce parametric uncertainty into experimental and damage identification metrics. Thereby offering valuable insights to elevate the efficacy of monitoring and control techniques. These comprehensive tests also encapsulate paramedic uncertainty, providing robust support for applications in uncertainty quantification, stochastic modelling, and supervised and unsupervised machine learning methodologies.http://www.sciencedirect.com/science/article/pii/S2352340924000179Structural health monitoringDamage detectionExperimental practicesUncertainty quantificationMachine learning |
| spellingShingle | Amanda A.S.R. de Sousa Marcela R. Machado Experimental vibration dataset collected of a beam reinforced with masses under different health conditions Data in Brief Structural health monitoring Damage detection Experimental practices Uncertainty quantification Machine learning |
| title | Experimental vibration dataset collected of a beam reinforced with masses under different health conditions |
| title_full | Experimental vibration dataset collected of a beam reinforced with masses under different health conditions |
| title_fullStr | Experimental vibration dataset collected of a beam reinforced with masses under different health conditions |
| title_full_unstemmed | Experimental vibration dataset collected of a beam reinforced with masses under different health conditions |
| title_short | Experimental vibration dataset collected of a beam reinforced with masses under different health conditions |
| title_sort | experimental vibration dataset collected of a beam reinforced with masses under different health conditions |
| topic | Structural health monitoring Damage detection Experimental practices Uncertainty quantification Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2352340924000179 |
| work_keys_str_mv | AT amandaasrdesousa experimentalvibrationdatasetcollectedofabeamreinforcedwithmassesunderdifferenthealthconditions AT marcelarmachado experimentalvibrationdatasetcollectedofabeamreinforcedwithmassesunderdifferenthealthconditions |