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Since the aggregate structure influence the mechanical response of mixture and its monitoring signal, this study mainly advance the integration of gradation theory and sensor testing methods from the micro-scale analysis. Based on sphere parking theory, the upper and lower limit of weighted average size in gradations were determined from the point of densest/loosest state respectively. Three skeletons according to the weighted average size criterion were designed for comparatively analysis of their stability. Moreover, a smart rock(SR)sensor was embedded for monitoring and a skeleton-sensor coupling model was further established to reveal the mechanism of skeleton's evolution. Results show that although the weighted average size criterion is based on pure sphere assumption, it still works when comes to aggregates with real angular shapes. Key attributes of different skeletons can be captured and identified by the embedded sensor from the point of particle's sliding and rearrangement. Thus,apart from currant indices such as stress/strain etc,the movements data including acceleration and angular acceleration also shows a huge potential possibility in pavement monitoring in the future. • Muti-scale conversion in modelling based on HCP lattice was proposed. • Pure sphere parking theory is verified in angular aggregate skeletons. • Damage characteristic is revealed by skeleton-sensor coupling analysis. • Application of SR sensor is validated in capturing skeletons' micro-states. [ABSTRACT FROM AUTHOR]