A clustering approach for structural health monitoring on bridges

  • Alberto Diez
  • Nguyen Lu Dang Khoa
  • Mehrisadat Makki Alamdari
  • Yang Wang
  • Fang Chen
  • Peter Runcie
Original Paper


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.


Structural health monitoring Damage detection Novelty detection Unsupervised learning K-means clustering 



The main author would like to thank National ICT Australia for the great support to this work and during his internship, as part of the work on his diploma thesis. The authors also wish to thank the Road and Maritime Services (RMS) in New South Wales for provision of the support and testing facilities for this research work.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Alberto Diez
    • 1
  • Nguyen Lu Dang Khoa
    • 2
  • Mehrisadat Makki Alamdari
    • 2
  • Yang Wang
    • 2
  • Fang Chen
    • 2
  • Peter Runcie
    • 2
  1. 1.Tecnalia Research & InnovationDonostia-San SebastiánSpain
  2. 2.National ICT Australia (NICTA)SydneyAustralia

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