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A Systematic Review of Optimization Algorithms for Structural Health Monitoring and Optimal Sensor Placement.

Sahar Hassani1, Ulrike Dackermann1

  • 1Centre for Infrastructure Engineering and Safety, School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW 2052, Australia.

Sensors (Basel, Switzerland)
|March 30, 2023
PubMed
Summary
This summary is machine-generated.

Optimization algorithms enhance structural health monitoring (SHM) systems by optimizing sensor placement (OSP) for improved data quality and reduced costs. Advanced AI methods offer accurate and efficient solutions for complex SHM challenges.

Keywords:
optimal sensor placementoptimization algorithmsstructural health monitoring

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Area of Science:

  • Engineering
  • Computer Science

Background:

  • Structural health monitoring (SHM) is crucial for the sustainability and serviceability of complex structures.
  • Designing effective SHM systems requires optimizing sensor configurations, data handling, and analysis techniques.
  • Optimization algorithms are key to achieving optimal system settings and maximizing data quality.

Purpose of the Study:

  • To comprehensively review recent optimization algorithms applied to SHM and optimal sensor placement (OSP).
  • To define SHM components, OSP problem formulation, and various optimization methodologies.
  • To explore the application of these methodologies in SHM systems and OSP.

Main Methods:

  • Review and synthesis of existing literature on SHM, OSP, and optimization algorithms.
  • Categorization and comparison of different optimization techniques (e.g., random search, heuristic algorithms, AI).
  • Analysis of how these algorithms are applied to SHM system design and OSP.

Main Results:

  • The application of optimization algorithms in SHM and OSP is increasingly common and sophisticated.
  • These methods significantly impact data quality, information density, and overall system performance.
  • AI-driven methods demonstrate high accuracy and speed in solving complex SHM problems.

Conclusions:

  • Optimization algorithms are essential for developing advanced SHM systems.
  • AI-based optimization offers powerful solutions for achieving efficient and accurate structural health monitoring.
  • Continued development in this area promises enhanced structural safety and longevity.