Integrated Framework for Bridge Crack Detection and Semantic BIM Model Generation Using Drone-Captured Imagery and Deep Learning Techniques
Publication: ASCE Inspire 2023
ABSTRACT
Concrete cracking in bridges significantly endangers their safety and integrity. Traditional crack detection methods, reliant on human visual inspection, are labor-intensive and prone to errors. This paper introduces a unique framework for bridge crack detection and integration with building information models (BIM), trialed on a 423-ft bridge in Atlanta, Georgia. The framework comprises two main stages: (1) creating a BIM model using drone-captured images and structure from motion (SFM) photogrammetry, and (2) utilizing a deep learning-based encoder-decoder network to segment bridge cracks from orthomosaic images and superimpose these segmented cracks onto the BIM model. The suggested method showed robust performance, achieving a mean intersection over union (mIoU) of 0.787, precision of 0.751, and recall of 0.742. These results underline the potential of the proposed framework to improve the efficiency of bridge crack inspection processes.
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Information & Authors
Information
Published In
ASCE Inspire 2023
Pages: 167 - 175
History
Published online: Nov 14, 2023
ASCE Technical Topics:
- Architectural engineering
- Artificial intelligence and machine learning
- Bridge engineering
- Bridges
- Bridges (by material)
- Building information modeling
- Building management
- Computer programming
- Computing in civil engineering
- Concrete bridges
- Construction engineering
- Construction management
- Continuum mechanics
- Cracking
- Engineering fundamentals
- Engineering mechanics
- Fracture mechanics
- Infrastructure
- Infrastructure resilience
- Inspection
- Models (by type)
- Neural networks
- Solid mechanics
- Structural engineering
- Structural models
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