CPS: Medium: Collaborative Research: Scalable Intelligent Backscatter-Based RF Sensor Network for Self-Diagnosis of Structures

CPS：中：协作研究：用于结构自诊断的可扩展智能反向散射射频传感器网络

• 批准号: 2038761
• 负责人: Branko Glisic
• 金额: $ 40万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2038761&HistoricalAwards=false
• 关键词: CPS; Medium; Collaborative; Research; Scalable

This Cyber-Physical Systems (CPS) grant will advance structural health monitoring of concrete structures by relying on data acquired by a novel sensing technology with unprecedented scalability and spatial resolution. Modern society depends critically on sound and steadfast functioning of a variety of engineering structures and infrastructures, such as bridges, buildings, pipelines, geotechnical structures, aircrafts, wind turbines, and industrial facilities. Due to aging, massive urbanization, and climate change, there is a growing need for accurate and reliable assessment of the health condition, performance, and operation of these structures in order to ensure their continuous functioning and safe use. The researched technology enables pervasive and scalable sensing of concrete structures with high resolution by transforming concrete into a smart self-sensing material, thereby enabling reliable long-term structural health monitoring. This in turn contributes to the nation’s sustainability and resilience and to advancing the nation’s prosperity, welfare, and security. The project advances multiple core research areas in structural health monitoring including CPS system architectures using embedded devices, multi-parameter sensing and networking based on radio frequency sensors, and machine learning for accurate and reliable data analytics. The research outcomes are highly translational to various other CPS domains. The project also contributes to secondary education and outreach activities in multiple ways as well as to undergraduate and graduate education. The aim of this project is to create a novel sensing system comprised of radio frequency sensors that are pervasively embedded in large volumes of concrete structures and that sense their localities using radio frequency properties. The objective is the assessment of key parameters that reflect the behavior of the monitored structure under operational conditions, such as deformation, temperature, and humidity, as well as detection and characterization of damages. The project has the following intellectual contributions: 1) Passive radio frequency-based sensing that operates over a wide range of frequencies; architectures of smart exciters and networked radio frequency sensors that communicate among themselves via backscatter modulation; solar-powered radio frequency exciter platform that powers the sensors. 2) Energy-based sensing and network optimization of the radio frequency sensor network in terms of its monitoring ability and network connectivity given the constraints on the available harvested power at the exciters. 3) Machine learning methods for function estimation based on the principle of ensemble modeling with Gaussian processes and applied to self-localization and to inference of three-dimensional distributions of material parameters within large volumes of concrete structures.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.

该网络物理系统（CPS）赠款将通过依靠具有前所未有的可伸缩性和空间分辨率的新型传感技术获得的数据来改进混凝土结构的结构健康监测。现代社会急剧取决于各种工程结构和基础设施的声音和坚定运作，例如桥梁，建筑物，管道，岩土工程结构，飞机，风力涡轮机和工业设施。由于衰老，大规模的城市化和气候变化，对这些结构的健康状况，性能和操作的准确评估越来越需要，以确保其连续的功能和安全使用。研究的技术通过将混凝土转换为智能的自感应材料，从而实现了具有高分辨率的混凝土结构的普遍和可扩展性敏感性，从而实现了可靠的长期结构健康监测。反过来，这有助于国家的可持续性和韧性，并促进国家的繁荣，福利和安全。该项目在结构健康监测中的多个核心研究领域推进了包括CPS系统架构，使用嵌入式设备，基于射频传感器的多参数传感器和网络，以及机器学习，以进行准确可靠的数据分析。研究结果高度转化为其他各种CPS域。该项目还以多种方式以及本科和研究生教育的方式为中学教育和外展活动做出了贡献。该项目的目的是创建一个新型的传感器系统，该系统完整地完成了射频传感器，这些传感器普遍地嵌入了大量混凝土结构中，并使用射频属性来感知其位置。目的是评估关键参数，这些参数反映了在操作条件下（例如变形，温度和湿度）以及损坏的检测和表征的行为。该项目具有以下智力贡献：1）基于无线电频率的传感器，可在各种频率上运行；通过反向散射调制进行智能激发器和网络射频传感器的体系结构；太阳能射频激发仪平台为传感器提供动力。 2）基于能量的感应和网络优化射频传感器网络的监视能力和网络连接性，鉴于激发器可用的收获功率的限制。 3）基于高斯过程的集合建模的原理，应用于自定位，并推断大量混凝土结构内物质参数的三维分布，该奖项反映了NSF的法定任务，并通过使用基金会的智力效果进行评估，该奖项反映了NSF的法定任务。

项目成果

Roadmap on measurement technologies for next generation structural health monitoring systems

• DOI: 10.1088/1361-6501/acd135
• 发表时间: 2023-09-01
• 期刊: MEASUREMENT SCIENCE AND TECHNOLOGY
• 影响因子: 2.4
• 作者: Laflamme，Simon;Ubertini，Filippo;Milillo，Pietro
• 通讯作者: Milillo，Pietro