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

这项网络物理系统（CPS）拨款将通过依靠具有前所未有的可扩展性和空间分辨率的新型传感技术获得的数据来推进混凝土结构的结构健康监测。现代社会主要依赖于各种工程结构和基础设施（如桥梁、建筑物、管道、岩土结构、飞机、风力涡轮机和工业设施）的良好和稳定运行。由于老龄化、大规模城市化和气候变化，越来越需要对这些结构的健康状况、性能和运行进行准确可靠的评估，以确保其持续运行和安全使用。研究的技术通过将混凝土转化为智能自传感材料，实现了对混凝土结构的高分辨率、普遍和可扩展的传感，从而实现了可靠的长期结构健康监测。这反过来又有助于国家的可持续性和复原力，并促进国家的繁荣、福利和安全。该项目推进了结构健康监测的多个核心研究领域，包括使用嵌入式设备的CPS系统架构，基于射频传感器的多参数传感和网络，以及用于准确可靠数据分析的机器学习。研究成果是高度翻译到各种其他CPS域。该项目还以多种方式促进中等教育和外联活动，以及本科和研究生教育。该项目的目的是创建一个由射频传感器组成的新型传感系统，这些传感器普遍嵌入大量混凝土结构中，并使用射频特性来感知它们的位置。其目的是评估反映受监测结构在运行条件下的行为的关键参数，如变形、温度和湿度，以及损伤的检测和表征。该项目具有以下智力贡献：1）在广泛频率范围内工作的基于无源射频的传感;智能激励器和联网射频传感器的架构，它们通过反向散射调制相互通信;为传感器供电的太阳能射频激励器平台。2)射频传感器网络在其监测能力和网络连接性方面的基于能量的感测和网络优化，给定激励器处的可用收获功率的约束。3)基于高斯过程集成建模原理的功能估计机器学习方法，并应用于自定位和大量混凝土结构内材料参数三维分布的推断。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.