Next Generation Self-Sensing Asphalt Concretes for Road Digitalization

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Deterioration leads to a substantial increase in the necessity for additional materials and heightened investments in order to effectively maintain and upgrade asphalt roads. The NextSAC project aims to train Dr. Yuanyuan Li as a leading researcher, who will be supervised by a finely integrated supervisory team. NextSAC initiative's primary outcomes include: 1) electrical model for prognosticating the electrical parameters of SACs; 2) Mechanical model for the damage behavior prediction of SACs; 3) Electro-mechanical model for road damage self-sensing; and 4) Industrial application of transformative self-sensing materials and technologies. The theoretical training of Dr. Li will cover interdisciplinary fields of the numerical modeling, infrastructure material, data analysis, electrical modelling, asphalt material and pavement engineering. The compulsory skills training in project management, intellectual property, commercialization, organization, teaching, supervision, networking, communication and financial management, will strongly support the career development of the fellow. An imperative requirement of timely self-sensing of the damage condition is urgently needed for promptly recommending appropriate maintenance strategies and immediate solutions to prolong the asphalt pavements service life. Such proactive measures will undoubtedly reduce waste asphalt materials, consumption of non-renewable resources, and maintenance investments. Subsequent to the fellowship, the individual will attain a substantial proficiency in scientific mechanisms and their practical application in the field of self-sensing materials and technologies. The deliverables outcomes will construct a strong foundation for roads maintenance digitalization, generating a series of positive contributions to the EU across scientific, social, environmental and economic areas.