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: 2329802
• 负责人: Milutin Stanacevic
• 金额: $ 9万
• 依托单位: SUNY at Stony Brook
• 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=2329802&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.

现代社会批判性地取决于其运输基础设施，尤其是道路网络。因此，越来越需要对道路的结构健康状况进行准确，可靠的评估，特别是对道路的地下课程（层）负责道路结构强度和性能的准确评估。满足这些需求的方法必须是普遍，可扩展的，可持续的，无线的，低成本，低功率，高分辨率和可部署的长时间，并且对课程的干扰可忽略不计。当前，现有的监视技术无法以一种或另一种方式满足这些要求。该项目旨在开发解决这一挑战的基础技术。提出的技术的关键推动器是基于无线反向散射的微小无电射频传感器（BBRS），它们在使用反向散射调制过程中进行通信时感知自己之间的通信通道。 BBR测量了通信链路的相位和放大器，从而辨别各种材料特性，并能够同时监测距离，相对位移，应变，裂纹，僵硬，刚度，湿度和温度的整个地下课程。 BBR由安装在移动车辆上的激励器提供的外部RF信号提供动力；相同的RF信号可为载体提供反向散射。信息通过MultiHop网络在BBR之间提供。 BBR能够进行一些基本的数据处理。该项目的目的是生成初步结果，以证明对嵌入式BBR启用的多参数，几乎连续监视路面地下课程的可行性；可以使用移动兴奋剂和接收器从数千个纯净的嵌入式BBR中读取的协议；和物理知识的机器学习算法，用于评估当前状况和受监控课程的性能，并为其对时间的预测建模。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力优点和更广泛的影响标准通过评估来评估的支持。