A clustering approach for structural health monitoring on bridges

Diez Oliván, Alberto; Dang Khoa, Nguyen Lu; Makki Alamdari, Mehrisadat; Wang, Yang; Chen, Fang y Runcie, Peter (2016). A clustering approach for structural health monitoring on bridges. "Journal of Civil Structural Health Monitoring", v. 3 (n. 6); pp. 429-445. ISSN 2190-5452. https://doi.org/10.1007/s13349-016-0160-0.


Título: A clustering approach for structural health monitoring on bridges
  • Diez Oliván, Alberto
  • Dang Khoa, Nguyen Lu
  • Makki Alamdari, Mehrisadat
  • Wang, Yang
  • Chen, Fang
  • Runcie, Peter
Tipo de Documento: Artículo
Título de Revista/Publicación: Journal of Civil Structural Health Monitoring
Fecha: Julio 2016
Volumen: 3
Palabras Clave Informales: Structural health monitoring, Damage detection, Novelty detection, Unsupervised learning, K-means clustering
Escuela: E.T.S.I. Industriales (UPM)
Departamento: Otro
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Structural health monitoring is a process for identifying damage in civil infrastructures using sensing system. It has been increasingly employed due to advances in sensing technologies and data analytic using machine learning. A common problem within this scenario is that limited data of real structural faults are available. Therefore, unsupervised and novelty detection machine learning methods must be employed. This work presents a clustering based approach to group substructures or joints with similar behaviour on bridge and then detect abnormal or damaged ones, as part of efforts in applying structural health monitoring to the Sydney Harbour Bridge, one of iconic structures in Australia. The approach is a combination of feature extraction, a nearest neighbor based outlier removal, followed by a clustering approach over both vibration events and joints representatives. Vibration signals caused by passing vehicles from different joints are then classified and damaged joints can be detected and located. The validity of the approach was demonstrated using real data collected from the Sydney Harbour Bridge. The clustering results showed correlations among similarly located joints in different bridge zones. Moreover, it also helped to detect a damaged joint and a joint with a faulty instrumented sensor, and thus demonstrated the feasibility of the proposed clustering based approach to complement existing damage detection strategies.

Más información

ID de Registro: 46840
Identificador DC: http://oa.upm.es/46840/
Identificador OAI: oai:oa.upm.es:46840
Identificador DOI: 10.1007/s13349-016-0160-0
URL Oficial: https://link.springer.com/article/10.1007/s13349-016-0160-0
Depositado por: Memoria Investigacion
Depositado el: 16 Jun 2017 16:28
Ultima Modificación: 31 Jul 2017 22:30
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