Shear loading detection of through bolts in bridge structures using a percussion‐based one‐dimensional memory‐augmented convolutional neural network

Abstract

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.

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