I-Corps: AI-Based Decision Support for Management of Bridge Networks

University Of Illinois At Urbana-Champaign, Urbana IL

Investigators

Abstract

The broader impact/commercial potential of this I-Corps project is the development of decision support software systems for optimal management of transportation networks. These systems address risks associated with network assets as well as the emergent traffic dynamics that would likely ensue in natural hazards. Planners from various entities, including state agencies and municipalities may use this system for optimal asset management for transportation infrastructure, based on the current conditions of the physical infrastructure and the predicted high priority areas. This decision support system also may enhance evacuation planning and post-disaster network management procedures, as it can provide mobility maps that are updated with incoming data during and after a disaster. This I-Corps project is based on the development of graph neural network (GNN) models that may be used to estimate the network response under probabilistic natural hazards with minimal computational time. These neural network models are designed to calculate various response measures, such as connectivity, shortest distance, and travel times. The modular feature of GNNs allows for models that are developed using data from networks in one city or region to be usable in another region. These models may be used for extreme events such as earthquakes, floods, hurricanes, and tornadoes. The proposed technology is building upon successful numerical experiments on transportation networks in California, New York, and Florida for seismic and flood hazards. The accuracy, computational efficiency, and robustness of these GNN models are documented. The decision support that will be built on these GNN models will feature intelligent tools that evaluate the current conditions of the physical infrastructure, rank investment decisions related to network planning to focus on high priority concerns. It also may facilitate effective emergency response by combining the same predictive capabilities with real-time network data to effectively update response plans. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.