Abstract
The Mean Profile Depth (MPD) is the synthetic index worldwide used to describe the macrotexture of road pavement surfaces. MPD is evaluated from two dimensional profiles captured by macrotexture laser-based devices, by means of ISO (International Organization for Standardization) or ASTM (American Society for Testing and Materials International) algorithms.
Several macrotexture laser-based measuring devices are present in the world market and, usually each one provides different MPD values also for the same road pavement. Furthermore the Standard Algorithm application produces MPD values affected by a wide variability. For these reasons the comparison of MPD values deriving from different laser-based macrotexture measuring devices is unsatisfying and it can produces deep misunderstanding on road macrotexture characterization.
In order to reduce the MPD variability and to improve the MPD values comparison a new algorithm to evaluate a more stable macrotexture synthetic index (Estimated Texture Depth) ETD, has been proposed. It has been developed with more than two hundred profiles belonging to virtual pavements and it has been validated on fifteen real road profiles.
The results seems to be promising: the Square Weight Estimated Texture Depth (ETDsw) provides more stable and reliable MPD values and in the same time, it improves the agreement between macrotexture values provided by different laser-based devices.
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Acknowledgement
The authors wish to acknowledge the Virginia Polytechnic Institute and State University, Professor Gerardo Flintsch, director of the Center for Sustainable Transportation Infrastructure, and the staff of the Virginia Tech Transportation Institute for allowing us to use the data collected on the Virginia Smart Road.
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D’Apuzzo, M., Evangelisti, A., Santilli, D., Nicolosi, V. (2020). Theoretical Development and Validation of a New 3D Macrotexture Index Evaluated from Laser Based Profile Measurements. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12251. Springer, Cham. https://doi.org/10.1007/978-3-030-58808-3_27
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DOI: https://doi.org/10.1007/978-3-030-58808-3_27
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