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Toward a Structural Health Monitoring Methodology for Concrete Structures under Dynamic Loads Using Embedded FBG

Alejandra Amaya1, Julián Sierra-Pérez1

  • 1Grupo de Investigación en Ingeniería Aeroespacial, Escuela de Ingenierias, Universidad Pontificia Bolivariana, Sede Central Medellín, Circular 1 70-01, Medellín 050031, Colombia.

Sensors (Basel, Switzerland)
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PubMed
Summary

This study introduces a novel method for structural health monitoring (SHM) in reinforced concrete using fiber optic sensors and pattern recognition. The technique accurately detects minor damages by analyzing sensor data, even with minimal strain changes.

Keywords:
data drivenfiber Bragg gratings (FBGs)fiber optic sensors (FOSs)pattern recognitionreinforced concrete structuresstructural health monitoring (SHM)

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Area of Science:

  • Civil Engineering
  • Materials Science
  • Sensor Technology

Background:

  • Structural health monitoring (SHM) is crucial for maintaining the integrity of reinforced concrete structures.
  • Existing SHM methods often struggle to detect subtle damages.
  • Fiber optic sensors offer a promising solution for embedded structural monitoring.

Purpose of the Study:

  • To develop and validate a data-driven methodology for SHM in reinforced concrete structures.
  • To investigate the effectiveness of embedded fiber optic sensors and pattern recognition for damage detection.
  • To assess the capability of the system in identifying minor stiffness changes indicative of damage.

Main Methods:

  • A prototype reinforced concrete structure was constructed and instrumented with Fiber Bragg Gratings (FBGs) bonded to reinforcing steel bars.
  • Dynamic loading was applied using a shaker, and superficial damages were induced.
  • Data from pristine and damaged states were collected and analyzed using Mahalanobis distance-based classifiers for supervised and unsupervised pattern recognition.

Main Results:

  • The developed pattern recognition techniques achieved up to 98% accuracy in damage detection.
  • The sensing scheme successfully identified slight stiffness changes caused by damage.
  • The system demonstrated effectiveness even when strains were minimal and damage-related changes were subtle.

Conclusions:

  • The proposed methodology provides an effective approach for SHM in reinforced concrete.
  • Embedded FBGs combined with advanced pattern recognition can reliably detect early-stage structural damage.
  • This technique enhances the safety and longevity assessment of concrete structures.