Structural health monitoring by combining machine learning and dimensionality reduction techniques
Visualitza/Obre
10.23967/j.rimni.2018.12.001
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/165275
Tipus de documentArticle
Data publicació2019
EditorUniversitat Politècnica de Catalunya. CIMNE
Condicions d'accésAccés obert
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Reconeixement-NoComercial-CompartirIgual 3.0 Genèrica
Abstract
Structural Health Monitoring is of major interest in many areas of structural mechanics. This paper presents a new approach based on the combination of dimensionality reduction and data-mining techniques able to differentiate damaged and undamaged regions in a given structure. Indeed, existence, severity (size) and location of damage can be efficiently estimated from collected data at some locations from which the fields of interest are completed before the analysis based on machine learning and dimensionality reduction techniques proceed.
CitacióQuaranta, G. [et al.]. Structural health monitoring by combining machine learning and dimensionality reduction techniques. "Revista internacional de métodos numéricos para cálculo y diseño en ingeniería", 2019, vol. 35, núm. 1.
ISSN0213-1315
1886-158X
1886-158X
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RIMNI350108.pdf | 9,945Mb | Visualitza/Obre |