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Structural health monitoring by combining machine learning and dimensionality reduction techniques

Article dans une revue avec comité de lecture
Author
QUARANTA, Giacomo
LOPEZ, Elena
10921 Institut de Recherche en Génie Civil et Mécanique [GeM]
111023 École Centrale de Nantes [ECN]
ABISSET-CHAVANNE, Emmanuelle
111023 École Centrale de Nantes [ECN]
10921 Institut de Recherche en Génie Civil et Mécanique [GeM]
DUVAL, Jean Louis
HUERTA, Antonio
81618 Laboratori de Càlcul Numèric (LACAN) [LaCàN]
86136 Departament de Matematica Aplicada III [Barcelona]
CHINESTA, Francisco
86289 Laboratoire Procédés et Ingénierie en Mécanique et Matériaux [PIMM]

URI
http://hdl.handle.net/10985/15522
DOI
10.23967/j.rimni.2018.12.001
Date
2019
Journal
Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería

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.

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