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Detection Method for Bolted Connection Looseness at Small Angles of Timber Structures based on Deep Learning.

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This study introduces a cost-effective deep learning and machine vision method for detecting bolt looseness in timber structures. The developed technique accurately identifies small bolt angle changes, ensuring structural integrity.

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Area of Science:

  • Structural Engineering
  • Computer Vision
  • Machine Learning

Background:

  • Bolt looseness is a critical failure factor in timber structures.
  • Existing detection methods often lack a balance between cost and accuracy.
  • Accurate monitoring of bolted connections is essential for structural safety.

Purpose of the Study:

  • To develop a precise and cost-effective method for detecting small bolt loosening angles in timber structures.
  • To leverage deep learning and machine vision for enhanced structural health monitoring.
  • To validate the proposed method's effectiveness on multi-bolted connections.

Main Methods:

  • Utilized deep learning and machine vision technology for bolt looseness detection.
  • Trained the Single Shot MultiBox Detector (SSD) algorithm on image datasets.
  • Evaluated three recognition schemes targeting nut specification numbers, rectangular marks, and circular marks.

Main Results:

  • The scheme identifying circular marks achieved the smallest identification angle error of 0.38°.
  • Further improvements led to a minimum recognition angle accuracy of 1°.
  • The method demonstrated feasibility and high accuracy on four- and eight-bolted connections.

Conclusions:

  • The proposed deep learning and machine vision approach offers a viable solution for detecting bolt looseness in timber structures.
  • This method achieves a favorable balance between low operating cost and high accuracy.
  • The technique is effective for monitoring both single and multi-bolted connections, enhancing structural safety.