Field investigation of novel self-sensing asphalt pavement for weigh-in-motion sensing

• Published in: Smart materials and structures 2022-08, Vol.31 (8), p.85004
• Main Authors: Borke Birgin, Hasan, D’Alessandro, Antonella, Favaro, Maurizio, Sangiorgi, Cesare, Laflamme, Simon, Ubertini, Filippo
• Format: Article
• Language: English
• Subjects: artificial neural networks; field investigation; machine learning; sensing pavement; smart carbon-doped sustainable materials; traffic monitoring; weigh-in-motion
• ISSN: 0964-1726; 1361-665X
• DOI: 10.1088/1361-665X/ac7922

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.