Computer Vision-Based Estimation of The Effects of Vibration in Slipform Paving

Abstract

Slipform paving is a complex multistep process encompassing concrete pouring, vibration, consolidation, correction, and curing. One of the most critical aspects in the process is that the frequency and duration of vibration can influence the distribution of coarse aggregate particles in the cured concrete. However, there is still a fundamental lack of methodologies to formally characterize the interrelationships between vibration characteristics and the quality of the cured pavement. This study aims to address this issue by proposing a novel toolset for the assessment of the spatial features of coarse aggregate distribution in doweled pavements. This system is based on paver consolidation simulations and employs computer vision as the primary tool for analysis. The experimental protocol, recently developed by the authors, employs coarse aggregate particles color-coded by gradation to aid in recognizing the distribution of different granulometric sizes within the specimens. High-resolution, two-dimensional digital images obtained from cured specimens were used to analyze aggregate characteristics, orientation, and dispersion, with particular emphasis on quantifying the effect of the distance from the vibrator head on such parameters. The results showed that the proposed digital imaging processing techniques were capable of quantitatively characterizing the distribution of coarse aggregate particles in the specimens. Moreover, novel metrics were proposed to characterize the quality of the consolidation based on the digital image data.