Improving Culvert Condition Prediction Models Using Federated Learning: The Case Study of Utah
Publication: Construction Research Congress 2024
ABSTRACT
Departments of Transportation (DOTs) are constantly looking for more efficient ways to inspect culverts. This study proposes a data-driven approach based on federated learning for culvert inspection in Utah. As the Utah DOT (UDOT) had a limited dataset of culverts in Utah, we collected data from several other state DOTs. However, instead of using traditional centralized machine learning techniques, we used a federated learning approach in which data from other DOTs was not shared with UDOT, but rather developed local models based on those datasets were shared. This allows us to keep the data of the DOTs private while benefiting from their collective knowledge. As a result of using federated learning, the performance of UDOT’s culvert condition prediction model was improved by 16%. The study demonstrates that DOTs can mutually benefit in scenarios of data scarcity, while still adhering to their preference for not sharing data directly with one another.
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Information & Authors
Information
Published In
Construction Research Congress 2024
Pages: 359 - 368
History
Published online: Mar 18, 2024
ASCE Technical Topics:
- Artificial intelligence and machine learning
- Case studies
- Computer programming
- Computing in civil engineering
- Construction engineering
- Construction management
- Culverts
- Data analysis
- Data collection
- Engineering fundamentals
- Infrastructure
- Inspection
- Methodology (by type)
- Pipeline systems
- Pipes
- Research methods (by type)
- Transportation engineering
- Transportation management
- Transportation studies
Authors
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