带裂纹保护下采样器的两级疲劳裂纹检测框架

IF 6.8 2区材料科学 Q1 ENGINEERING, MECHANICAL

International Journal of Fatigue Pub Date : 2025-09-03 DOI:10.1016/j.ijfatigue.2025.109179

Andrii Kompanets , Remco Duits , Davide Leonetti , H.H. (Bert) Snijder

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引用次数: 0

摘要

钢结构桥梁的检测是维护结构完整性和保障公共安全的重要手段。利用神经网络对疲劳裂纹进行可视化检测是提高结构可靠性和运行效率的重要途径。这通常使用多个神经网络来完成，以确保结果的可靠性。为此，本文提出了一种结合两种神经网络的两阶段钢桥图像裂纹检测与分级框架，并对其进行了详细分析。此外，由于表面裂纹宽度较小，标准图像降采样方法对于裂纹检测任务可能不是最优的。因此，图像降采样是实现裂纹自动检测的重要步骤。在这项工作中，引入了一种保持裂纹的下采样方法，该方法旨在对图像进行下采样，从而使（我们的两阶段）钢桥图像中的裂纹检测显示出更高的性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Two-stage fatigue crack detection framework with crack-preserving downsampler

Inspection of steel bridges is essential for maintaining structural integrity and ensuring public safety. Automation of such inspections using neural networks for the visual detection of fatigue cracks is a prominent way to improve structural reliability and operational efficiency. This is often done using multiple neural networks to ensure the reliability of the results. Therefore, in this work, a two-stage crack detection and sizing framework for images of steel bridges is proposed and analysed in detail, which combines two neural networks. Additionally, it is shown that standard image downsampling methods can be non-optimal for the crack detection task because of the small width of the cracks at the surface. Hence, image downsampling is an important step for automatic crack detection. This is applied to the images contained in the Cracks in Steel Bridges (CSB) dataset In this work, a crack-preserving downsampling method is introduced, which is designed to downsample images in such a way that (our two-stage) crack detection in images of steel bridges shows higher performance.

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来源期刊

International Journal of Fatigue 工程技术-材料科学：综合

CiteScore

10.70

自引率

21.70%

发文量

619

审稿时长

58 days

期刊介绍： Typical subjects discussed in International Journal of Fatigue address: Novel fatigue testing and characterization methods (new kinds of fatigue tests, critical evaluation of existing methods, in situ measurement of fatigue degradation, non-contact field measurements) Multiaxial fatigue and complex loading effects of materials and structures, exploring state-of-the-art concepts in degradation under cyclic loading Fatigue in the very high cycle regime, including failure mode transitions from surface to subsurface, effects of surface treatment, processing, and loading conditions Modeling (including degradation processes and related driving forces, multiscale/multi-resolution methods, computational hierarchical and concurrent methods for coupled component and material responses, novel methods for notch root analysis, fracture mechanics, damage mechanics, crack growth kinetics, life prediction and durability, and prediction of stochastic fatigue behavior reflecting microstructure and service conditions) Models for early stages of fatigue crack formation and growth that explicitly consider microstructure and relevant materials science aspects Understanding the influence or manufacturing and processing route on fatigue degradation, and embedding this understanding in more predictive schemes for mitigation and design against fatigue Prognosis and damage state awareness (including sensors, monitoring, methodology, interactive control, accelerated methods, data interpretation) Applications of technologies associated with fatigue and their implications for structural integrity and reliability. This includes issues related to design, operation and maintenance, i.e., life cycle engineering Smart materials and structures that can sense and mitigate fatigue degradation Fatigue of devices and structures at small scales, including effects of process route and surfaces/interfaces.