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Structural Health Monitoring of Composite Pipelines Utilizing Fiber Optic Sensors and an AI-Based Algorithm-A

Wael A Altabey1,2, Zhishen Wu1, Mohammad Noori3,4

  • 1International Institute for Urban Systems Engineering (IIUSE), Southeast University, Nanjing 210096, China.

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|April 28, 2023
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Summary

A new structural health monitoring (SHM) system uses an Enhanced Convolutional Neural Network (ECNN) with artificial intelligence (AI) to accurately detect early-stage damage in composite pipelines. This AI-driven approach offers reliable early warning for pipeline integrity.

Keywords:
Convolutional Neural Network (CNN)Fiber Bragg grating (FBG) sensory systemcomposite pipelinesdamage detectiondeep learningstructural health monitoring (SHM)

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Area of Science:

  • Materials Science and Engineering
  • Artificial Intelligence in Engineering
  • Structural Health Monitoring

Background:

  • Composite pipelines, such as basalt fiber reinforced polymer (BFRP), are susceptible to damage.
  • Existing Fiber Bragg Grating (FBG) sensor systems face challenges in accurately detecting pipeline damage.
  • Early detection of damage is crucial for ensuring the safety and longevity of pipelines.

Purpose of the Study:

  • To propose an integrated sensing-diagnostic structural health monitoring (SHM) system for early damage detection in composite pipelines.
  • To develop and implement an artificial intelligence (AI)-based algorithm using an Enhanced Convolutional Neural Network (ECNN) for damage identification.
  • To enhance damage detection accuracy by replacing the traditional softmax layer with a k-Nearest Neighbor (k-NN) algorithm for inference.

Main Methods:

  • Development and calibration of finite element models based on experimental pipe measurements under damage.
  • Assessment of strain distribution patterns under internal pressure and burst conditions using calibrated models.
  • Implementation of an AI-based algorithm (ECNN with k-NN inference) trained to identify pipe deterioration and detect damage initiation.

Main Results:

  • The developed ECNN model demonstrated excellent agreement with experimental strain data, showing an average error of 0.093% compared to FBG sensor data.
  • The proposed SHM system achieved high performance metrics: 93.33% accuracy, 91.18% regression rate, and 90.54% F1-score.
  • The AI-driven approach effectively predicts pipe damage mechanisms using distributed strain patterns.

Conclusions:

  • The integrated sensing-diagnostic SHM system utilizing an ECNN with AI offers a reliable and accurate method for early damage detection in composite pipelines.
  • The novel ECNN architecture with k-NN inference significantly improves the performance of damage detection without the need for model retraining.
  • This AI-based SHM system provides a robust solution for automatic early warning, enhancing pipeline safety and maintenance strategies.