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Epistemic and aleatoric uncertainty quantification for crack detection using a Bayesian Boundary Aware Convolutional Network

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  • Rathnakumar, Rahul
  • Pang, Yutian
  • Liu, Yongming

Abstract

Accurately detecting crack boundaries is crucial for reliability assessment and risk management of structures and materials, such as structural health monitoring, diagnostics, prognostics, and maintenance scheduling. Uncertainty quantification of crack detection is challenging due to various stochastic factors, such as measurement noises, signal processing, and model simplifications. A machine learning-based approach is proposed to quantify both epistemic and aleatoric uncertainties concurrently. We introduce a Bayesian Boundary-Aware Convolutional Network (B-BACN) that emphasizes uncertainty-aware boundary refinement to generate precise and reliable crack boundary detections. The proposed method employs a multi-task learning approach, where we use Monte Carlo Dropout to learn the epistemic uncertainty and a Gaussian sampling function to predict each sample’s aleatoric uncertainty. Moreover, we include a boundary refinement loss to B-BACN to enhance the determination of defect boundaries. The proposed method is demonstrated with benchmark experimental results and compared with several existing methods. The experimental results illustrate the effectiveness of our proposed approach in uncertainty-aware crack boundary detection, minimizing misclassification rate, and improving model calibration capabilities.

Suggested Citation

  • Rathnakumar, Rahul & Pang, Yutian & Liu, Yongming, 2023. "Epistemic and aleatoric uncertainty quantification for crack detection using a Bayesian Boundary Aware Convolutional Network," Reliability Engineering and System Safety, Elsevier, vol. 240(C).
  • Handle: RePEc:eee:reensy:v:240:y:2023:i:c:s0951832023004611
    DOI: 10.1016/j.ress.2023.109547
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    References listed on IDEAS

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    1. Zhou, Taotao & Han, Te & Droguett, Enrique Lopez, 2022. "Towards trustworthy machine fault diagnosis: A probabilistic Bayesian deep learning framework," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    2. Zhu, Rong & Chen, Yuan & Peng, Weiwen & Ye, Zhi-Sheng, 2022. "Bayesian deep-learning for RUL prediction: An active learning perspective," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    3. McFarland, John & DeCarlo, Erin, 2020. "A Monte Carlo framework for probabilistic analysis and variance decomposition with distribution parameter uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
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    Citations

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    Cited by:

    1. Cai, Yu & Zhao, Wei & Wang, Xiaoping & Ou, Yanjun & Chen, Yangyang & Li, Xueyan, 2024. "A novel multiple linearization method for reliability analysis based on evidence theory," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    2. He, Yu & Ma, Yafei & Huang, Ke & Wang, Lei & Zhang, Jianren, 2024. "Digital twin Bayesian entropy framework for corrosion fatigue life prediction and calibration of bridge suspender," Reliability Engineering and System Safety, Elsevier, vol. 252(C).
    3. Rathnakumar, Rahul & Liu, Yongming, 2025. "Towards safer general aviation operations using a vision-based decision support system for weather threat avoidance," Journal of Air Transport Management, Elsevier, vol. 123(C).
    4. Zhang, Chen & Bahrami, Mahdi & Mishra, Dhanada K. & Yuen, Matthew M.F. & Yu, Yantao & Zhang, Jize, 2025. "SelectSeg: Uncertainty-based selective training and prediction for accurate crack segmentation under limited data and noisy annotations," Reliability Engineering and System Safety, Elsevier, vol. 259(C).

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