The integration of weigh-in-motion (WIM) sensors within highways or bridge structural health monitoring systems is becoming increasingly popular to ensure structural integrity and users safety. Compared to standard technologies, smart self-sensing materials and systems present a simpler sensing setup, a longer service life, and increased durability against environmental effects. Field deployment of such technologies requires characterization and design optimization for realistic scales. This paper presents a field investigation of the vehicle load-sensing capabilities of a newly developed low-cost, eco-friendly and high durability smart composite paving material. The novel contributions of the work include the design and installation of a full-scale sensing pavement section and of the sensing hardware and software using tailored low-cost electronics and a learning algorithm for vehicle load estimation. The outcomes of the research demonstrate the effectiveness of the proposed system for traffic monitoring of infrastructures and WIM sensing by estimating the gross weight of passing trucks within a 20% error during an autonomous sensing period of two months.

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 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 requiring 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 machine learning 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.