The through bolt, which can be used as a shear connector, has attracted more attention since several accelerated bridge construction methods have been applied to renovate damaged bridges and construct new ones. Because current methods for shear loading detection of through bolts require constant deployment of sensors, the percussion‐based method may be a better alternative to improve the practicality and reduce costs. However, to process percussion‐induced sound signals, current percussion‐based methods all employ machine learning (ML) techniques that depend on manual extraction and classification of features. Attempting to solve this issue, we propose a one‐dimensional, memory‐augmented convolutional neural network (1D‐MACNN) inspired by the memory‐augmented neural network (MANN), which is the main computational novelty of this paper. Particularly, the proposed 1D‐MACNN has capacity to address new scenarios from unknown distributions, that is, the testing categories have not been seen during the training. By directly feeding the raw percussion‐induced sound signals into the 1D‐MACNN, the shear loading of through bolts can be detected. Compared to current ML‐based and deep learning‐based methods for one‐dimensional (1D) signals (e.g., 1D convolutional neural network and 1D convolutional neural network–long short‐term memory), the advantage of our proposed 1D‐MACNN is that it can achieve better performance. Specifically, the proposed 1D‐MACNN can achieve accuracy of 1, precision of 1, recall of 1, and F1‐score of 1. Moreover, the proposed 1D‐MACNN can effectively address the issue of new categories without retraining (in terms of two new categories: accuracy = .83; precision = .89; recall = .77; F1‐score = .83). Finally, the experimental results demonstrate the effectiveness of the 1D‐MACNN, which has great potential to detect shear loading of through bolts in bridge structures.

中文翻译：

---

基于敲击的一维记忆增强卷积神经网络对桥梁结构中贯穿螺栓的剪切载荷检测

由于已经采用了几种加速桥梁的施工方法来修复受损的桥梁并建造新的桥梁，因此可以用作剪切连接器的贯穿螺栓引起了更多的关注。因为当前用于检测通孔螺栓的剪切载荷的方法需要不断部署传感器，所以基于打击乐器的方法可能是更好的选择，可以提高实用性并降低成本。但是，为了处理由打击乐产生的声音信号，当前所有基于打击乐的方法都采用机器学习（ML）技术，该技术依赖于手动提取和特征分类。为了解决这个问题，我们提出了一种受记忆增强神经网络（MANN）启发的一维，记忆增强卷积神经网络（1D-MACNN），这是本文的主要计算新颖性。特别是，建议的1D-MACNN具有处理未知分布中的新方案的能力，也就是说，在培训期间未看到测试类别。通过将原始的由打击乐器引起的声音信号直接馈入一维MACNN中，可以检测通孔螺栓的剪切载荷。与当前针对一维（1D）信号的基于ML和基于深度学习的方法（例如1D卷积神经网络和1D卷积神经网络-长短期记忆）相比，我们提出的1D-MACNN的优势在于它可以获得更好的性能。具体来说，拟议的1D-MACNN可以达到1的精度，精度为1，召回率为1，F1分数为1。此外，拟议的1D-MACNN可以有效地解决新类别的问题，而无需重新训练（就两个而言）新类别：精度= .83；精度=。89; 召回率= 0.77; F1分数= 0.83）。最后，实验结果证明了1D‐MACNN的有效性，它在检测桥梁结构中贯穿螺栓的剪切载荷方面具有巨大的潜力。