Heuristic Optimization for Digital Twin Modeling of Existing Bridges from Point Cloud Data by Parametric Prototype Models

Digital twins (DTs) can support the operation and maintenance process of bridges by providing a digital model representing the actual asset in reality. The underlying semantic-geometric model of bridges can be created from point cloud data (PCD), obtained by laser scanning or photogrammetry. The bridge PCD, however, needs to be processed and abstracted to a parametric model to handle geometric updates. Today, this process is conducted manually which in turn increases the geometric modeling costs. This paper aims to automate semantic segmentation and parametric modeling as essential steps in the geometric modeling of bridges. The point cloud of bridges is semantically segmented first through a deep-learning model. The value of parameters is then extracted by a heuristic optimization algorithm. Finally, the model of the entire bridge is created. The results of the paper show that the geometric modeling process of bridges can be automated to a large extent through computational methods.