ABSTRACT
This paper presents a new surface sensing approach for health monitoring of Asphalt Concrete (AC) pavements utilizing a new class of self-powered wireless sensors. The proposed method was based on the interpretation of the data stored in the memory gates of the sensor. A three-dimensional finite element analysis was performed to obtain the dynamic strain at the surface of the pavement for different damage scenarios. Damage states were defined using the element weakening method. The sensor output data was generated from the time-history of the surface strains. Thereafter, the sensor data was fitted to a Gaussian Mixture Model (GMM) in order to define an initial damage indicator features. Finally, probabilistic neural network classification scheme was used to classify the damage states. The results indicate that the proposed method is effective in detecting and classifying bottom-up cracks in AC pavements using a surface-mounted network of sensors.
