This article presents the preliminary results of the implementation of artificial vision and image processing techniques in order to obtain conventional parameters to characterize asphalts such as the expansion ratio (ER by-Expantion Ratio) and the halflife (HL by-Half Life), in addition to unconventional parameters such as the collapse curve and the bubble size distribution (BSD by-Bubble Size Distibution) of an asphalt foam. The ER and HL can be obtained from the collapse curve, therefore, to estimate this curve, a 2D method based on the image geometry is proposed. For the estimation of the bubble size and therefore the BSD, a method based on Maximally Stable Extremal Regions is proposed. The results indicate that the method for height estimation gives good results for what is valid and in terms of estimating the bubble size, the method works, but requires adjustments to obtain better results.
This document presents the software development of a data capture and recording system for the characterization of foamed asphalt in laboratory tests. The system was developed using a reduced board computer, specifically the Raspberry Pi 2 Model B. The software prototype includes a graphical interface that allows the management and interaction with the user in charge of carrying out the tests. The system allows generating calibration files for various distance sensors; it also includes the data capture system for the tests and the graphical data visualization system for further analysis.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.