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

• Published in: Sustainability Vol. 14; no. 17; p. 10837
• Main Authors: Li, Zhi-Jun, Adamu, Kabiru, Yan, Kai, Xu, Xiu-Li, Shao, Peng, Li, Xue-Hong, Bashir, Hafsat Muhammad
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2022
• Subjects: Accuracy; Algorithms; Artificial intelligence; Bolts; Bridge failure; Bridges; Bridges, Iron and steel; Classification; Cracks; Deep learning; Equipment and supplies; Human error; Hypotheses; Machine learning; Maintenance and repair; Manufacturing; Methods; Neural networks; Nuts (fasteners); Short term memory; Statistical analysis; Statistical tests; Steel; Structural health monitoring; Support vector machines; Time series; Truss bridges; Unmanned aerial vehicles; Variance analysis; China
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su141710837

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.