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项目的更广泛的影响/商业潜力是开发决策支持软件系统，用于运输网络的最佳管理。这些系统解决了与网络资产有关的风险以及可能在自然危害中随之而来的新兴交通动态。包括州机构和市政当局在内的各个实体的规划人员可能会根据当前的物理基础设施的当前条件和预测的高优先级领域，将该系统用于运输基础设施的最佳资产管理。该决策支持系统还可以增强疏散计划和后策划后网络管理程序，因为它可以提供灾难期间和之后随输入数据更新的机动性图。此I-CORPS项目基于图形神经网络（GNN）模型的开发，这些模型可用于估算与最小值计算时间的概率自然危害下的网络响应。这些神经网络模型旨在计算各种响应措施，例如连通性，最短距离和旅行时间。 GNNS的模块化功能允许使用来自另一个城市或地区的网络数据开发的模型，可在另一个地区使用。这些模型可用于极端事件，例如地震，洪水，飓风和龙卷风。拟议的技术是基于在加利福尼亚州，纽约和佛罗里达州的运输网络上成功进行的数值实验，以实现地震和洪水危害。记录了这些GNN模型的准确性，计算效率和鲁棒性。将建立在这些GNN模型上的决策支持将采用智能工具，以评估与网络计划有关的物理基础设施的当前条件，排名投资决策，以关注高优先级问题。它还可以通过将相同的预测能力与实时网络数据相结合以有效地更新响应计划来促进有效的紧急响应。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，认为值得通过评估来获得支持。