Detection of Nut–Bolt Loss in Steel Bridges Using Deep Learning Techniques

Abstract

The early detection of bolts and nuts’ loss on bridges has a huge tendency of averting bridge collapse. The aim of this research is to develop a novel framework for the detection of bolt–nut losses in steel bridges using deep learning techniques. The objectives include: to design a framework for the detection of nuts and bolts and nut holes using deep learning techniques, to implement the designed framework using Python programming, and to evaluate the performance of the designed framework. Convolutional neural network (CNN) and long- and short-term memory (LSTM) techniques were employed using 8 × 8 blocks of images of bridges as inputs. Based on the proposed models, which considered the CNN in its ordinary form, and combined with the LSTM and You Only Look Once (YOLOv4) algorithms, the CNN achieved average classification accuracy of 95.60% and the LSTM achieved an accuracy of 93.00% on the sampled images. The YOLOv4 algorithm, which is a modified version of the CNN with single forward propagation, was utilized, and the detection accuracy was 76.5%. The relatively high level of detection accuracy recorded by the CNN is attributed to its stepwise extraction by convolution and pooling processes. However, a statistical test of the hypothesis at the 5.0% level of significance revealed that there was no statistically significant difference between object detection and classifications among the models used in the built framework. Therefore, the use of the CNN model is recommended for the detection of nuts and bolts and nut holes on steel truss bridges for effective structural health monitoring (SHM) purposes based on its high level of detection accuracy and speed.

Operational Modal Analysis on Bridges: A Comprehensive Review

“…Chamangard et al [313] utilized compact one-dimensional (1D) CNNs with transfer learning to detect damage accurately, even with limited training data, achieving high accuracy when sufficient data were available. Li et al [314] combined a CNN with short-and long-term memory neural networks to detect bolt-nut losses in steel bridges. Khodabandehlou et al [315] used a CNN to predict the predefined damage states (including extent and location) with accuracy using vibration response data from a reinforced concrete highway bridge model.…”

Deep Learning for Structural Health Monitoring: Data, Algorithms, Applications, Challenges, and Trends

“…The CNN algorithm involved includes Two-Dimensional Convolutional Neural Networks (2D-CNN), You Look Only Once (YOLO), U-Net, Region-based Convolutional Neural Networks (R-CNN), Fully Convolutional Networks (FCN), Mask Region-Based Convolutional Neural Networks (Mask R-CNN), etc. Li et al [38] used the sliding window method to cover the image of the steel bridge and used convolution and pooling methods in CNN to classify the steel bridge's bolts, nuts, and nut holes. Kao et al [36] used YOLOv4 to identify bridge cracks through boundary frame selection and extracted crack contours using the edge detection method to achieve crack quantification.…”

Detection of Missing Bolts for Engineering Structures in Natural Environment Using Machine Vision and Deep Learning

“…However, they did not consider the effects of natural wear and corrosion on the bolts, and they did not account for the difference between the bolts and the background. Li et al [ 27 ] generated images and videos from different locations of a steel truss bridge in Wuxi, Jiangsu Province, China, but the dataset environment was homogeneous, limiting the bolt recognition model’s generalizability. Yu et al [ 21 ] and Sun et al [ 22 ] acquired bolt images using smartphones at a distance of 8 to 10 cm.…”