Machine Learning-Based Risk Assessment for Bridge Infrastructure in Indiana
Publication: ASCE Inspire 2023
ABSTRACT
About 6% of 19,284 bridges in Indiana are rated structurally deficient. The deck area of structurally deficient bridges accounts for 3.3% of the total deck area on all structures. This deteriorating infrastructure impedes the development of a sustainable and resilient society. Therefore, there is a significant need to evaluate the risk for optimized infrastructure investment. We conduct a risk assessment for Indiana’s bridges by applying machine learning-based deterioration models with the support vector machine (SVM) and Gaussian process regression (GPR) algorithms. The machine learning-based risk assessment is able to generate a reliable risk analysis tool for bridge infrastructure in Indiana. Results from the study can provide stakeholders and decision-makers with an efficient and effective way to evaluate the performance and status of the bridge infrastructure. The outcomes of the study can be used to prioritize investment in repairing bridges based on different risk levels, save the cost of restoration, and extend the service life of the bridge infrastructure.
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Information & Authors
Information
Published In
ASCE Inspire 2023
Pages: 426 - 432
History
Published online: Nov 14, 2023
ASCE Technical Topics:
- Artificial intelligence and machine learning
- Bridge decks
- Bridge tests
- Computer programming
- Computing in civil engineering
- Decks
- Deterioration
- Disaster risk management
- Engineering fundamentals
- Equipment and machinery
- Field tests
- Infrastructure
- Infrastructure resilience
- Infrastructure vulnerability
- Materials characterization
- Materials engineering
- Risk management
- Structural engineering
- Structural systems
- Tests (by type)
Authors
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