Three-dimensional modeling and defect quantification of existing concrete bridges based on photogrammetry and computer aided design

Bridge infrastructure is aging and deteriorating and requires innovative condition monitoring and assessment to ensure safety and maintain serviceability. Current practices for bridge condition assessment depend mainly on visual inspection. Innovative data collection technologies can assist in bridge inspection and defect identification and circumvent the limitations of traditional visual inspection. This research aims to apply photogrammetry principles and image processing techniques to develop a fullscale three-dimensional (3D) bridge structure model for defect identification and quantification. The data source to establish the point cloud models is a set of digital images taken at the bridge site. The point cloud models are developed by analyzing high-resolution images captured using a Nikon D4S camera. The images are captured from different positions/locations to provide extensive stereo coverage and maximum overlapping. Two hundred images of the bridge are processed with PhotoModeler software along with the calibration information, and ground control points (GCPs) coordinates to create the models. A standard digital close-range image processing is implemented to generate the 3D points cloud model. The calibration and orientation processes are made over a deformed situation. However, the developed CAD bridge drawings depict the original undeformed geometry. The extracted bridge inventory data from the 3D model, including bridge geometry information and bridge damage quantification, can provide a baseline for the bridge monitoring system to detect bridge deterioration over the years. The developed photogrammetric 3D model has an RMSE of 0.0081 m and uncertainty of +/- 0.0041 m. Future research can aim at developing reusable parametric objects for bridges in Revit and coding scripts using programming languages such as Dynamo or Python to perform further analysis of the captured information.(c) 2023 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/ by-nc-nd/4.0/).