KEYWORDS: Signal processing, Sensing systems, Roads, Signal generators, Vehicle sensing, Structural health monitoring, Sensors, Machine learning, Data processing, Carbon
The integration of load tracking systems for structural health monitoring of road infrastructures is a big leap towards future smart cities, allowing efficient policy making for sustainable development and maintenance, also, supporting fast decision taking with respect to unforeseen events. To facilitate deployment, the load sensing system should be low-cost, easily applicable to any type of road infrastructure, highly durable, and require minimum maintenance. In this context, the authors introduce a novel weigh-in-motion (WIM) system for load sensing and vehicle characterization. The integration of the proposed WIM system with proper signal processing techniques enables the real-time alert of traffic conditions, for example overloads. The sensor is a smart road pavement that generates voltage signals upon the passage of a vehicle. The voltage signals are generated through the piezoresistive effect of the material produced by the inclusion of carbon micro fibers. These signals are processed to generate information about vehicle weight, speed, and class. Preliminary results from field tests show the promise of this technology at WIM sensing.
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