The electrification and digitalization of transportation is one of the most important challenges in achieving sector decarbonization, which is responsible for nearly one-fifth of greenhouse gas emissions in Europe. One of the issues related to road electrification is the adherence between pavement surfaces and embedded electronic devices (sensors, charging units, etc.). This study presents a methodology for evaluating the adherence between asphalt surfaces and electronic elements, characterizing the morphology of surfaces using a laser scanner as a Non-Destructive Testing (NDT) technique. The main objective is to optimize the application of tack coat and define the appropriate adhesive type based on the specific surface type present on the road. Adherence tests were conducted using various types of adhesives with different surface morphologies, including asphalt mixtures and materials for electronic device coating. Surface texture measurement was performed using a laser scanner. The results of this study have direct implications for ecologically sustainable and efficient road management. Optimizing the application of tack coat based on texture measurement allows for timely maintenance interventions. Additionally, the incorporation of embedded electronic elements in the pavement for continuous monitoring of road conditions contributes to more sustainable and efficient management practices. Overall, this research provides valuable insights into adherence between asphalt surfaces and embedded electronic devices. The methodology utilizing a laser scanner offers a promising approach for optimizing adherence and contributing to efficient road management.
Nowadays, the use of Non-destructive Testing Methods (NDTs) has been consolidated for the Structural Health Monitoring (SHM) of road pavements and the continuous and rapid assessment of transport infrastructures. These methods are crucial to support Public Authorities and infrastructure managers in the decision-making processes, for programming more effective maintenance actions. Pavement instrumentation with different kinds of embedded sensors is generally employed to acquire long-term monitoring data. However, several limitations of intrusive sensors are related to the risk of premature damage and deterioration. Amongst the most recent advances in NDT methods, the use of asphalt-based self-sensing materials has progressively emerged as a promising technique for the ground-based health monitoring of road pavements. These kinds of stimuli-responsive materials can be designed by dispersing conductive carbon-based nanomaterials throughout the host-insulating asphalt pavement. More specifically, the proposed NDT sensing methodology is based on the piezoresistive effect, consisting of a change in the electrical response of the pavement when subjected to strain or damage. Real-time reliable data about the structural health condition of road pavements can be therefore obtained by measuring the electrical response of the pavement, implementing a sensing procedure. This research aims at assessing the strain and load sensing response of piezoresistive asphalt mixtures. To this purpose, electromechanical laboratory tests were conducted to evaluate the electrical response of asphalt mixtures under dynamic loading. A tailored digital signal processing and machine learning algorithms were also developed to analyze the electrical signal generated by the material and provide insightful information about its structural behavior.
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