Detecting and localising damage based on image recognition and structure from motion, and reflecting it in a 3D bridge model

• Published in: Structure and infrastructure engineering Vol. 20; no. 4; pp. 594 - 606
• Main Authors: Yamane, Tatsuro, Chun, Pang-jo, Honda, Riki
• Format: Journal Article
• Language: English
• Published: Taylor & Francis 02.04.2024
• Subjects: 3-D model; Building information modeling; damage detection; deep learning; maintenance; structure from motion
• ISSN: 1573-2479; 1744-8980
• DOI: 10.1080/15732479.2022.2131845

To ensure the safe operation of bridges, it is necessary to carry out repair and strengthening based on appropriate inspections and damage records. However, in conventional bridge maintenance, the inspection results are recorded as 2 D data, which makes it difficult to carry out appropriate repair and strengthening due to the small amount of information. Therefore, research has been conducted to utilize Building Information Modeling (BIM) for bridge maintenance to record damage in more detail, and various concepts have been proposed. However, since no concrete method has been established to automate the entire process from damage detection to linking the damage to the BIM-based model, these tasks currently have to be carried out manually. For this reason, the use of BIM in actual maintenance work has not progressed. This study proposes a method to automate the process from damage detection to linkage to a BIM-based model. In the proposed method, each damage is detected using deep learning, local location information is obtained using Structure from Motion, and the location information is integrated into the 3 D data of the entire bridge. In addition, a case study using an actual bridge is also presented to demonstrate the usefulness of the proposed method.