首页|Study Findings on Machine Learning Are Outlined in Reports from University of Brasilia (Experimental vibration dataset collected of a beam reinforced with masses under different health conditions)
Study Findings on Machine Learning Are Outlined in Reports from University of Brasilia (Experimental vibration dataset collected of a beam reinforced with masses under different health conditions)
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Elsevier
New research on artificial intelligence is the subject of a new report. According to news reporting from Brasilia, Brazil, by NewsRx journalists, research stated, “Vibration signals extracted from structures across diverse health conditions have become indispensable for monitoring structural integrity.” Funders for this research include Coordenacao De Aperfeicoamento De Pessoal De Nivel Superior; Cnpq; Narodowe Centrum Nauki; Horizon 2020. Our news correspondents obtained a quote from the research from University of Brasilia: “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.”
University of BrasiliaBrasiliaBrazilSouth AmericaCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
Elsevier
