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Type: Artigo de Periódico
Title: An SHM approach using machine learning and statistical indicators extracted from raw dynamic measurements
Author: Finotti, Rafaelle Piazzaroli
Cury, Alexandre Abrahão
Barbosa, Flávio de Souza
Resumo: -
Abstract: Structural Health Monitoring using raw dynamic measurements is the subject of several studies aimed at identifying structural modifications or, more specifically, focused on damage assessment. Traditional damage detection methods associate structural modal deviations to damage. Nevertheless, the process used to determine modal characteristics can influence the results of such methods, which could lead to additional uncertainties. Thus, techniques combining machine learning and statistical analysis applied directly to raw measurements are being discussed in recent researches. The purpose of this paper is to investigate statistical indicators, little explored in damage identification methods, to characterize acceleration measurements directly in the time domain. Hence, the present work compares two machine learning algorithms to identify structural changes using statistics obtained from raw dynamic data. The algorithms are based on Artificial Neural Networks and Support Vector Machines. They are initially evaluated through numerical simulations using a simply supported beam model. Then, they are assessed through experimental tests performed on a laboratory beam structure and an actual railway bridge, in France. For all cases, different damage scenarios were considered. The obtained results encourage the development of computational tools using statistical indicators of acceleration measurements for structural alteration assessment.
Keywords: Structural dynamic
Damage identification
Computational intelligence
Structural health monitoring
Vibration monitoring
Dynamic measurement
CNPq: -
Language: eng
Country: Brasil
Publisher: -
Institution Initials: -
Access Type: Acesso Aberto
Issue Date: 14-Mar-2019
Appears in Collections:Artigos de Periódicos

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