Towards a more efficient and durable load classifier using machine learning analysis of electrical data generated by self-sensing asphalt mixtures

Real-time weighing plays a crucial role in various applications in the road infrastructure field, such as pavement design, pavement monitoring systems, and the development of digital twins. Innovative sensing technologies offer solutions to the limitations of traditional sensors, enhancing durability and cost-effectiveness. Current research on self-sensing technologies primarily focuses on material manufacturing, electrode embedding, and testing of piezoresistive properties. However, there is limited investigation of innovative methods for processing and analyzing electrical signals from self-sensing materials for load classification. The novelty of the present paper lies in the development of a machine learning-based load classifier using electrical data from laboratory self-sensing asphalt mixtures with carbon fibers (CFs). Specimens underwent repeated cyclic dynamic loads, and electrical signals were recorded and processed using a customized digital processing algorithm. Artificial Neural Network (ANN) and decision tree algorithms were employed to develop load classification models. Results indicate that the addition of CFs strengthens the asphalt mixture, converting it into a piezoresistive material with load-sensing capabilities. Conclusions show that while the machine learning algorithms performed satisfactorily, further research is required for optimization purposes.