3D vision-based bolt loosening assessment using photogrammetry, deep neural networks, and 3D point-cloud processing. (1st July 2023)

• Record Type: Journal Article
• Authors: Pan, Xiao; Yang, T.Y.
• Is Part Of: Journal of building engineering. Volume 70(2023)
• Publication Date: 2023-07-01
• Subjects: Structural health monitoring -- Structural bolt loosening -- 3D computer vision -- Deep learning -- 3D point cloud processing; Building -- Periodicals; 690.05
• DOI: 10.1016/j.jobe.2023.106326
• Languages: English
• ISSNs: 2352-7102

Abstract

Abstract: Structural bolts are essential structural elements. Detection of structural bolt loosening is of great importance to provide earlier warnings of structural damages and prevent catastrophic system-level collapse. Most existing studies about bolt loosening assessment were built in 2D computer vision, where the assessment may be restricted based on the camera views. In this paper, a novel 3D vision-based methodology is proposed for autonomous bolt loosening assessment. First, a 3D point cloud of bolted connection is created using readily available 2D images. Second, a new convolutional neural network (CNN)-based method is developed to localize structural bolts in the 3D point cloud. Further, a 3D point cloud processing algorithm is developed to recognize and quantify bolt loosening. Parameter studies were conducted to investigate the effectiveness of the proposed pipeline. Finally, a real-world implementation has been conducted to quantify bolt loosening on a steel column base connection with bolts. The results indicate that the proposed bolt loosening assessment methodology can effectively localize and quantify bolt loosening at high accuracy and low cost. Highlights: First-of-its-kind bolt loosening assessment by 3D vision and point cloud processing. A new multi-view vision-based method for 3D bolt localization. A new 3D point cloud processing algorithm for bolt loosening assessment. Successful implementation of the pipeline in a real-world bolt connection.