Pattern Recognition in the National Bridge Inventory for Automated Screening and the Assessment of Infrastructure

The size and complexity of the problem of maintaining the aging U.S. transportation infrastructure system, combined with the shortage of resources, necessitates an efficient strategy to prioritize the allocation of funds. Within the suite of tools available for decision-making for bridges, a fundamental characteristic is safe load carrying capacity. This capacity measure typically requires knowledge and data on the structural details of the constituent members to enable predictions of available resistance relative to loading demands. Bridges that receive low ratings and are deemed incapable of carrying the required loads are “posted” with maximum weight limit signage. This paper introduces a data-driven solution that enables the automated, rapid, and cost-effective evaluation of load postings for large infrastructure networks. The method proposed in this paper involves leveraging the large bridge population in the national bridge inventory and the associated bridge descriptors such as geometrical, operational, functional, and physical features, to extract and define patterns for predicting posting status. A cost-sensitive random forest classification algorithm was trained on over 280,000 bridges in selected categories in the national bridge inventory including steel, reinforced concrete, prestressed concrete, and timber bridges. Performance evaluation of the models demonstrated the validity of the models and comparisons with a number of other common classifiers was presented. The trained models were capable of detecting posted and unposted bridges with an average error of about 11% and 16% respectively. The trade-off between safety and economy in the models was also studied. Finally, as a product of the data-driven approach, an interactive software interface was developed which accepts user input data on bridges and predicts the posting status. This tool is expected to provide an intuitive method for rapid screening of bridge inventories and estimating deterioration progression, thereby resulting in substantial safety and financial benefits to owners.