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Project Details

Title:Investigating the Applicability of Multi-Fidelity Modeling to Condition Evaluation of Transportation Infrastructure
Principal Investigators:Rebecca Atadero and Yanlin Guo
University:Colorado State University
Grant #:69A3551747108 (FAST Act)
Project #:MPC-618
RiP #:01732678
RH Display ID:11513
Keywords:asset management, bridges, condition surveys, data analysis, evaluation, infrastructure, life cycle costing


Evaluating the condition of transportation assets such as bridges is a critical and resource intensive part of the asset management process. Furthermore, information about the condition of assets may come from a variety of sources and some of the techniques that provide the greatest level of detail about condition are too time consuming or expensive to be practically applied to all structures. This research study will investigate the application of multi-fidelity modeling to evaluating the condition of transportation assets. Multi-fidelity modeling combines expensive high-fidelity data with low cost low-fidelity data to provide better predictions of condition at a lower cost. The objectives of this project are to, first, study ways of grouping bridges (or other assets) to allow for multi-fidelity modeling of a group; second, apply multi-fidelity modeling techniques using existing data sources; and third, evaluate the efficacy of multi-fidelity modeling in the context of transportation asset management considering the accuracy of predictions and lifecycle cost implications.

Project Word Files

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