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Automated Modal Analysis for Tracking Structural Change during Construction and Operation Phases.

Jun Teng1, De-Hui Tang2, Xiao Zhang3

  • 1School of Civil and Environmental Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China. tengj@hit.edu.cn.

Sensors (Basel, Switzerland)
|March 1, 2019
PubMed
Summary

This study enhances automated modal analysis (AMA) for structural health monitoring. An improved clustering technique and artificial neural networks (ANN) enable accurate tracking of bridge changes during construction.

Keywords:
automated modal analysis (AMA)continuous dynamic monitoringdensity-based spatial clustering of applications with noise (DBSCAN)system model ordertemperature effect

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Area of Science:

  • Structural Engineering
  • Vibrational Analysis
  • Machine Learning for Civil Infrastructure

Background:

  • Automated Modal Analysis (AMA) is crucial for detecting structural changes by tracking modal parameters.
  • Existing AMA techniques can be sensitive to noise and require robust methods for identifying reliable modal data.
  • Continuous monitoring is essential for understanding structural behavior during critical phases like construction.

Purpose of the Study:

  • To improve the accuracy and reliability of Automated Modal Analysis (AMA) for structural health monitoring.
  • To develop a robust method for cleaning modal data and selecting optimal model orders.
  • To enable continuous monitoring and assessment of structural integrity, specifically for bridges during construction.

Main Methods:

  • An improved density-based spatial clustering of applications with noise (DBSCAN) algorithm was developed to filter abnormal poles in stabilization diagrams.
  • System model order selection was optimized to enhance pole stability.
  • A continuous dynamic monitoring system integrated with the proposed algorithm and an artificial neural network (ANN) for temperature effect removal was implemented.

Main Results:

  • The improved DBSCAN effectively cleaned abnormal poles, leading to more stable modal parameters.
  • Numerical simulations and a full-scale arch bridge experiment validated the algorithm's effectiveness.
  • The integrated system successfully tracked structural changes during the bridge construction phase.
  • The ANN effectively removed temperature effects, allowing for the construction of a reliable health index.

Conclusions:

  • The proposed enhanced AMA technique, incorporating improved DBSCAN and ANN, offers a robust solution for structural health monitoring.
  • The method provides accurate tracking of structural changes and enables reliable health index construction under operational conditions.
  • This approach is highly effective for continuous monitoring of bridges, particularly during construction phases.