Heuristic Optimization for Digital Twin Modeling of Existing Bridges from Point Cloud Data by Parametric Prototype Models
Publication: Computing in Civil Engineering 2023
ABSTRACT
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.
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Information & Authors
Information
Published In
Computing in Civil Engineering 2023
Pages: 334 - 342
History
Published online: Jan 25, 2024
ASCE Technical Topics:
- Algorithms
- Architectural engineering
- Bridge engineering
- Bridge management
- Bridges
- Building management
- Computer networks
- Computing in civil engineering
- Design (by type)
- Engineering fundamentals
- Geometrics
- Highway and road design
- Maintenance and operation
- Mathematics
- Models (by type)
- Optimization models
- Parameters (statistics)
- Statistics
- Structural engineering
Authors
Metrics & Citations
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