CISE-MSI: DP: CNS: AI-powered Diagnosis Augmented by Self-sustaining Sensing System for Intelligent Wastewater Infrastructure Management

CISE-MSI：DP：CNS：通过自我维持传感系统增强人工智能诊断，实现智能废水基础设施管理

• 批准号: 2318641
• 负责人: Wenlu Wang
• 金额: $ 59.98万
• 依托单位: Texas A&M University Corpus Christi
• 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: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2318641&HistoricalAwards=false
• 关键词: CISE; MSI; DP; CNS; AI

Wastewater infrastructures are critical for modern cities, but aging sanitary sewer systems often suffer from defects like cracked pipes and damaged manholes, leading to the infiltration and inflow problem. This problem results in excessive surface runoff and groundwater flowing into the sewer system, causing sewer overflows and posing risks to public health and the environment. Current management approaches require significant time and expensive resources. This research project addresses this challenge by combining Graph Neural Networks and in-situ water pressure monitoring. The Graph Neural Networks surrogate model represents the urban wastewater system as a graph, allowing for efficient modeling of its temporal, spatial, and topological properties. Further by integrating a physical sensing system, this research enables accurate infiltration and inflow anomaly detection and predictions of cascading impacts with the Graph Neural Networks backbone allowing proactive and corrective actions to be taken in time.This research revolutionizes the management of urban wastewater systems by leveraging the interdisciplinary knowledge and expertise from hydrological & hydraulic sciences, embedded systems, and artificial intelligence. The technical outcomes of this research can significantly enhance public safety and health in coastal regions by improving the resilience of urban wastewater systems to climate change effects and facilitating quick recovery after natural hazards. By exploring the development of a fully sensed digital twin of the targeted wastewater system in south Texas, the project advances towards increased understanding and management capabilities of wastewater systems. The research results are closely integrated into the education and training of students. Besides, this project promotes the participation of underrepresented groups and K-12 students to pursue STEM studies. All designs are made publicly available to ensure equitable access to Artificial Intelligence-powered decision-making tools for broad implications and future research advances.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.

废水基础设施对现代城市至关重要，但老化的生活污水系统经常出现管道破裂、沙井损坏等缺陷，导致渗透和流入问题。这一问题导致过量的地表径流和地下水流入下水道系统，导致下水道溢出，对公众健康和环境构成风险。当前的管理方法需要大量时间和昂贵的资源。该研究项目通过结合图神经网络和现场水压监测来解决这一挑战。图神经网络代理模型将城市废水系统表示为图形，从而可以对其时间、空间和拓扑特性进行有效建模。此外，通过集成物理传感系统，这项研究能够通过图神经网络主干准确检测渗透和流入异常，并预测级联影响，从而能够及时采取主动和纠正措施。这项研究通过利用来自水文和水利科学、嵌入式系统和人工智能的跨学科知识和专业知识。这项研究的技术成果可以提高城市废水系统对气候变化影响的适应能力，并促进自然灾害后的快速恢复，从而显着提高沿海地区的公共安全和健康。通过探索德克萨斯州南部目标废水系统的完全感知数字孪生的开发，该项目朝着提高废水系统的理解和管理能力的方向发展。研究成果紧密融入学生的教育和培养。此外，该项目还促进弱势群体和 K-12 学生参与 STEM 学习。所有设计均公开，以确保公平地获得人工智能驱动的决策工具，以产生广泛的影响和未来的研究进展。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响进行评估，被认为值得支持审查标准。