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

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.