Bridge Load Posting Prediction

1600 (~12%) of the 13,000 bridges in Louisiana that facilitate movement of people, goods, and services are load posted, i.e. they are deemed to lack the strength to safely carry all legal loads. With time, bridges will age and deteriorate; at the same time, legal loads might also become heavier. In this context, it is essential to estimate the expected number of load posted bridges in future to allocate necessary resources during long term planning and maintenance scheduling. Therefore, the research goal of this project is to quantify the number of load posted bridges in Louisiana for the next 50 years by combining machine learning techniques, physics based deterioration models, and probabilistic methods. To this end, the National Bridge Inventory (NBI) database along with element level inspection data from Louisiana Department of Transportation and Development (LADOTD) (if available) will be used to gather data on bridges. Next, clustering techniques will be used to identify the key bridge parameters that have the most influence on load posting decisions. The future values of the key parameters will be determined using Markov chain models whose transition probability matrices will be developed using the available datasets and physics-based deterioration models. To estimate the probability of load posting on a bridge given its key parameters, logistic regression models will be developed. This model will be used along with future values of key parameters to estimate the load posting probability of each bridge for the next 50 years. These load posting probability estimates will be used in a Monte Carlo simulation-based methodology to estimate the expected number of load posted bridges, along with confidence interval estimates. During the implementation phase, an interactive tool will be developed, training manuals will be prepared, and training workshops will be conducted to facilitate the adoption of the research products. Additionally, the research outcomes will be disseminated via various platforms and outreach activities will be organized to involve middle and high school students to increase their interests in engineering careers.

• Record URL:
  • https://transet.lsu.edu/research-in-progress/
• Supplemental Notes:
  • 20STLSU01

Language

• English

Project

• Status: Completed
• Funding: $180122
• Contract Numbers:

  69A3551747106

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  Transportation Consortium of South-Central States (Tran-SET)

  Louisiana State University
  Baton Rouge, LA  United States  70803
• Project Managers:

  Mousa, Momen

• Performing Organizations:

  Louisiana State University

  Baton Rouge, Louisiana  United States  70803
• Principal Investigators:

  Kameshwar, Sabarethinam

• Start Date: 20200801
• Expected Completion Date: 20220201
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Bridges; Cluster analysis; Deterioration; Load limits; Logistic regression analysis; Machine learning; Markov chains; Monte Carlo method; Predictive models
• Geographic Terms: Louisiana
• Subject Areas: Bridges and other structures; Design; Highways; Maintenance and Preservation;

Filing Info

• Accession Number: 01757392
• Record Type: Research project
• Source Agency: Transportation Consortium of South-Central States (Tran-SET)
• Contract Numbers: 69A3551747106
• Files: UTC, RIP
• Created Date: Nov 9 2020 2:04PM