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Damage identification for large span structure based on multiscale inputs to artificial neural networks.

Wei Lu1, Jun Teng1, Yan Cui1

  • 1Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen 518055, China.

Thescientificworldjournal
|July 1, 2014
PubMed
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This study introduces a new method for structural health monitoring, combining local and global sensor data to identify damage in large structures. The approach is effective even in noisy conditions, enhancing structural safety assessments.

Area of Science:

  • Engineering
  • Computer Science

Background:

  • Structural health monitoring (SHM) systems are crucial for assessing the safety of large-span structures.
  • Limited research exists on damage identification using diverse sensor types in SHM for large structures.
  • Integrating multitype sensors or multiscale measurements is significant for comprehensive structural safety estimation.

Purpose of the Study:

  • To propose a novel methodology for damage identification in large-span structures.
  • To combine local and global measurements effectively within a structural health monitoring system.
  • To validate the approach in noisy environments and assess its robustness.

Main Methods:

  • Development of a methodology integrating local and global measurements.
  • Application of artificial neural networks (ANNs) for damage identification.

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  • Simulation of noisy environments at various levels to test the approach's effectiveness.

    • Main Results:

    • Validation of the methodology on a real large-span structure.
    • Successful damage placement identification.
    • Analysis of sensor quantity and optimal artificial neural network parameters.
    • Demonstration of robustness and effectiveness in simulated noisy conditions.

    Conclusions:

    • The proposed methodology effectively combines local and global measurements for damage identification in large-span structures.
    • The approach demonstrates robustness and effectiveness, even in noisy environments.
    • This research contributes to improving the reliability of structural health monitoring systems.