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

I-Corps：基于人工智能的桥梁网络管理决策支持

• 批准号: 2326446
• 负责人: Hadi Meidani
• 金额: $ 5万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2326446&HistoricalAwards=false
• 关键词: Corps; AI; Based; Decision; Support

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.

该 I-Corps 项目更广泛的影响/商业潜力是开发用于优化交通网络管理的决策支持软件系统。这些系统可解决与网络资产相关的风险以及自然灾害中可能出现的突发流量动态。包括国家机构和市政当局在内的各个实体的规划者可以根据物理基础设施的当前状况和预测的高优先级区域，使用该系统对交通基础设施进行优化资产管理。该决策支持系统还可以增强疏散规划和灾后网络管理程序，因为它可以提供在灾难期间和灾难后根据传入数据进行更新的移动地图。这个 I-Corps 项目基于图神经网络的开发（ GNN）模型可用于以最少的计算时间估计概率性自然灾害下的网络响应。这些神经网络模型旨在计算各种响应度量，例如连通性、最短距离和行程时间。 GNN 的模块化功能允许使用一个城市或地区的网络数据开发的模型可以在另一个地区使用。这些模型可用于地震、洪水、飓风和龙卷风等极端事件。拟议的技术建立在加利福尼亚州、纽约州和佛罗里达州交通网络针对地震和洪水灾害的成功数值实验的基础上。这些 GNN 模型的准确性、计算效率和鲁棒性都有记录。基于这些 GNN 模型的决策支持将采用智能工具来评估物理基础设施的当前状况，对与网络规划相关的投资决策进行排序，以重点关注高优先级问题。它还可以通过将相同的预测能力与实时网络数据相结合来有效地更新响应计划，从而促进有效的应急响应。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。