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Measurements of Strain01:27

Measurements of Strain

394
Strain quantifies the deformation of a material under force, typically measured as normal strain, which represents the change in length when compared with the original length. Electrical strain gauges are used for enhanced accuracy. These devices consist of a conductive wire mounted on a paper backing that adheres to the material's surface. These gauges operate on the piezoresistive effect, where the wire's electrical resistance changes in response to mechanical deformation. The strain...
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Investigations on the Deflection of Carbon-Reinforced Concrete Hollow-Core Slabs.

Materials (Basel, Switzerland)ยท2025
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Advances in Data Pre-Processing Methods for Distributed Fiber Optic Strain Sensing.

Bertram Richter1, Lisa Ulbrich2, Max Herbers1

  • 1Institute of Concrete Structures, TUD Dresden University of Technology, 01062 Dresden, Germany.

Sensors (Basel, Switzerland)
|December 17, 2024
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Summary

Distributed fiber optic sensors (DFOS) offer structural health monitoring (SHM) capabilities, but data disturbances like strain reading anomalies (SRAs) hinder analysis. This study presents advanced pre-processing algorithms to clean DFOS data, improving SHM accuracy.

Keywords:
algorithm benchmarkingautomationdata filteringdata pre-processingdata qualitydistributed fiber optic sensingsoftware developmentstructural health monitoring

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Area of Science:

  • Engineering
  • Materials Science
  • Sensor Technology

Background:

  • Distributed fiber optic sensors (DFOS) provide high spatial resolution for structural health monitoring (SHM).
  • Distributed strain sensing (DSS) data is susceptible to measurement disturbances like strain reading anomalies (SRAs), dropouts, and noise.
  • These disturbances impede accurate data evaluation and damage detection in engineering structures.

Purpose of the Study:

  • To discuss the characteristics and remediation of common disturbances in DSS data.
  • To present and evaluate advanced pre-processing algorithms for DSS data cleaning.
  • To provide a flexible and modular pre-processing workflow for SHM applications.

Main Methods:

  • Discussion of common disturbances: SRAs, dropouts, and noise.
  • Presentation of four advanced pre-processing algorithms: geometric threshold method (GTM), outlier-specific correction procedure (OSCP), sliding modified z-score (SMZS), and cluster filter.
  • Evaluation of algorithms using a realistic benchmark dataset simulating various measurement scenarios.

Main Results:

  • GTM, OSCP, and SMZS demonstrate promising results for SRA detection and removal.
  • The sliding average method is identified as inappropriate for SRA remediation.
  • Preserving crack-induced strain peaks is crucial for reliable crack monitoring.

Conclusions:

  • Effective pre-processing of DSS data is essential for successful SHM.
  • Advanced algorithms like GTM, OSCP, and SMZS significantly improve data quality.
  • The developed modular workflow enhances the reliability of damage detection using DFOS.