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Generalization of anomaly detection in bridge structures using a vibration‐based Siamese convolutional neural network
Computer-Aided Civil and Infrastructure Engineering ( IF 8.5 ) Pub Date : 2025-01-08 , DOI: 10.1111/mice.13411
Alireza Ghiasi, Zhen Zhang, Zijie Zeng, Ching Tai Ng, Abdul Hamid Sheikh, Javen Qinfeng Shi

Corrosion is one of the main damages in steel bridges, which appears as a loss of material and sectional area and causes member failure over time. A reliable bridge management system not only should help in preventing catastrophic structural failure by employing an in‐time anomaly detection approach for all the bridges within a network but also should reduce overall network costs commonly raised by expensive inspections. This paper proposes a deep learning approach to generalize anomaly detection due to section losses in steel bridges based on Siamese convolutional neural network (SCNN). A series of steel beams and bridges with various cross‐sections and lengths are considered to examine the performance of SCNN in generalizing anomaly detection in these structures. The study considered data from finite element simulations and experiments. The results reveal that the proposed integrated SCNN can detect anomalies successfully according to Australian standard AS7636 with reasonably high accuracy.

中文翻译:


使用基于振动的 Siamese 卷积神经网络对桥梁结构中的异常检测进行泛化



腐蚀是钢桥的主要损坏之一,表现为材料和截面积的损失,并随着时间的推移导致构件失效。可靠的桥梁管理系统不仅应该通过对网络内的所有桥梁采用及时的异常检测方法来帮助防止灾难性的结构故障,而且还应该降低通常由昂贵的检查引起的整体网络成本。本文提出了一种基于孪生卷积神经网络 (SCNN) 的深度学习方法,以推广钢桥截面损失引起的异常检测。考虑了一系列具有不同横截面和长度的钢梁和桥梁,以检查 SCNN 在这些结构中泛化异常检测的性能。该研究考虑了来自有限元模拟和实验的数据。结果表明,所提出的集成 SCNN 可以根据澳大利亚标准 AS7636 成功检测异常,并且具有相当高的准确率。
更新日期:2025-01-08
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