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An Integrated Machine Learning Algorithm for Separating the Long-Term Deflection Data of Prestressed Concrete

Xijun Ye1, Xueshuai Chen2, Yaxiong Lei3

  • 1School of Civil Engineering, Guangzhou University, Guangzhou 510006, China. xijun_ye@gzhu.edu.cn.

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Summary

This study introduces a machine learning algorithm to separate bridge deflection components. The method effectively isolates structural damage effects from operational and environmental influences for better bridge safety evaluation.

Keywords:
Butterworth filterEEMDFastICAPCAdeflection signal separationmachine learning

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Area of Science:

  • Structural Engineering
  • Machine Learning Applications
  • Civil Infrastructure Monitoring

Background:

  • Bridge deflection is crucial for safety evaluation but is affected by operational and environmental changes.
  • Measured deflection signals in prestressed concrete (PC) bridges are a superposition of multiple effects, masking damage-induced changes.
  • Distinguishing damage-related deflection from other factors like temperature and live loads is challenging.

Purpose of the Study:

  • To develop an integrated machine learning algorithm for separating individual deflection components from measured signals.
  • To accurately identify deflection components related to structural damage and material deterioration in PC bridges.
  • To improve the reliability of structural health monitoring by isolating damage-specific deflection signals.

Main Methods:

  • An integrated machine learning algorithm combining Butterworth filter, Ensemble Empirical Mode Decomposition (EEMD), Principle Component Analysis (PCA), and Fast Independent Component Analysis (FastICA).
  • Stage 1: Butterworth filter to separate high-frequency live load effects.
  • Stage 2-4: EEMD, PCA, and FastICA to decompose and extract intrinsic mode functions and independent deflection components.

Main Results:

  • Simulated results demonstrate successful separation of individual deflection components with noise levels below 10%.
  • The algorithm effectively isolates deflection components caused by structural damage, concrete shrinkage, creep, and prestress loss.
  • Practical application verification confirms the algorithm's feasibility for extracting damage-related structural deflection.

Conclusions:

  • The proposed integrated machine learning algorithm is effective for decomposing complex deflection signals in PC bridges.
  • This method enhances the ability to detect and assess structural damage by isolating its unique deflection signature.
  • The algorithm offers a robust solution for structural health monitoring, improving the accuracy of bridge safety evaluations.