i-BM: Bridge Management Tool [Brief]

Details

• Creators:
  Banaei-Kashani, Farnoush ; Rens, Kevin
• Corporate Creators:
  University of Colorado at Denver
• Corporate Contributors:
  Colorado Department of Transportation. Office of Applied Research
• Subject/TRT Terms:
  • Bridge management systems
  • Maintenance management
  • Software
  • Culverts
  • Bridges
  • Deterioration
• Resource Type:
  Brief
• Geographical Coverage:
  United States
• Edition:
  Research Brief
• Corporate Publisher:
  Colorado. Department of Transportation
• Abstract:
  Bridges and culverts deteriorate with time and use. While in the past, various data-driven deterioration models, including Bayesian models, probit model, and Markov chains are proposed in the literature to model bridge deterioration, these models either suffer from low accuracy or are too complex to be applicable. Recently deep learning (DL) AI models are shown to significantly outperform other analytical modeling methodologies in a variety of application domains. In the past we have developed several novel DL models for enhanced bridge and culvert deterioration forecasting. Our results show that DL-based models outperform other existing models.
• Format:
  PDF
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/84560/dot_84560_DS1.pdf
• File Type:
  [PDF - 260.88 KB]
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha-512:e6ad78a10ee308d0eaf5375ca0c70c19f567ffb3f1bdd49ea39d1bbe812e47a15a4dfff42587d2b56cb0526513f22d19133f9097572f19ea0372265d3b59edf5