A Deep Learning Model to Evaluate Cracks in the Underground Structure of New Domains
Publication: Geo-Congress 2024
ABSTRACT
Numerous studies are currently underway to investigate the safety management and evaluation of underground structures, as they require continuous management beyond their initial construction. Although such structures are regularly evaluated in accordance with safety regulations, measurements of degradation factors are typically performed manually by workers. To improve this process, many researchers have turned to computer vision, which offers an objective and automated method for evaluation. However, the performance of many deep learning models in this field is heavily dependent on training data, which may hinder their effectiveness in new domains. To address this issue, this study proposes a new method for detecting cracks on the surface of concrete underground structures based on computer vision. The proposed method achieves high performance with small additional labeling processes by utilizing both labeled and unlabeled datasets from the new domain. The results show that it contributes to objective and effective management with small additional processes in new domains.
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Information
Published In
Geo-Congress 2024
Pages: 415 - 424
History
Published online: Feb 22, 2024
ASCE Technical Topics:
- Artificial intelligence and machine learning
- Business management
- Computer programming
- Computing in civil engineering
- Construction engineering
- Construction management
- Continuous structures
- Continuum mechanics
- Cracking
- Design (by type)
- Drop structures
- Engineering fundamentals
- Engineering mechanics
- Fracture mechanics
- Neural networks
- Occupational safety
- Practice and Profession
- Public administration
- Public health and safety
- Safety
- Solid mechanics
- Special condition construction
- Structural design
- Structural engineering
- Structural safety
- Structures (by type)
- Underground construction
- Underground structures
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