Collaborative Research: EAGER: Reliable Monitoring and Predictive Modeling for Safer Future Smart Transportation Structures

合作研究：EAGER：可靠的监控和预测建模，打造更安全的未来智能交通结构

• 批准号: 2329801
• 负责人: Branko Glisic
• 金额: $ 3万
• 依托单位: Princeton University
• 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: 2023-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2329801&HistoricalAwards=false
• 关键词: Collaborative; Research; EAGER; Reliable; Monitoring

Modern societies depend critically on their transportation infrastructure, in particular on the networks of roads. Hence, there is a growing need for an accurate and reliable assessment of the structural health condition of roads, especially of their subsurface courses (layers) responsible for roads’ structural strength and performance. The approaches to addressing these needs must be pervasive, scalable, sustainable, wireless, low-cost, low-power, high-resolution, and deployable for long durations of time, with negligible disturbances to the courses. Currently, existing monitoring techniques fall short of fulfilling these requirements, in one way or another. The project seeks to develop foundational technology that addresses this challenge. The key enablers of the proposed technology are tiny wireless Backscatter-based, Batteryless, Radiofrequency Sensors (BBRS), which sense the communication channel between themselves while communicating using backscatter modulation. BBRS measure the phase and amplitude of the communication links, which allow discerning of various material properties, and enable simultaneous monitoring of distances, relative displacements, strain, cracking, stiffness, humidity, and temperature throughout continuums of subsurface courses. BBRS are powered by an external RF signal provided by exciters installed on moving vehicles; the same RF signal which supplies the carrier for the backscattering. Information is carried among BBRS via multihop networking. BBRS are able to carry out some basic data processing. The aim of this project is to generate preliminary results to demonstrate the feasibility of multiparameter, almost-continuous monitoring of pavement subsurface courses enabled by embeddable BBRS; protocols that enable readout from thousands of densely dispersed embedded BBRS using mobile exciters and receivers; and physics-informed machine learning algorithms for evaluation of current condition and performance of monitored courses, and for predictive modeling of their deterioration over time.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.

现代社会严重依赖其交通基础设施，特别是公路网。因此，越来越需要对道路的结构健康状况进行准确和可靠的评估，特别是对负责道路结构强度和性能的地下层(层)进行评估。满足这些需求的方法必须是无处不在的、可扩展的、可持续的、无线的、低成本的、低功耗、高分辨率的，并且可以长时间部署，对课程的干扰可以忽略不计。目前，现有的监测技术在某种程度上不能满足这些要求。该项目寻求开发应对这一挑战的基础技术。提出的技术的关键推动因素是基于后向散射的微型无线无电池射频传感器(BBR)，它在使用后向散射调制进行通信时感测彼此之间的通信通道。BBR测量通信链路的相位和幅度，从而能够识别各种材料属性，并能够同时监测整个地下连续层的距离、相对位移、应变、裂缝、硬度、湿度和温度。BBR由安装在移动车辆上的激励器提供的外部RF信号供电；同样的RF信号为后向散射提供载波。信息通过多跳网络在BBR之间承载。BBR能够执行一些基本的数据处理。该项目的目的是产生初步结果，以证明通过嵌入式BBR实现对路面地下课程的多参数、几乎连续监测的可行性；能够使用移动激励器和接收器从数千个密集分散的嵌入式BBR中读出数据的协议；以及用于评估被监测课程的现状和性能并对其随时间恶化进行预测建模的物理信息机器学习算法。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。