Improving pavement surface texture using laser scanning and numerical analysis

Date

2019-08

Authors

Kouchaki, Sareh

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Abstract

Safe roads need pavement surfaces that provide an adequate level of skid resistance to reduce accidents, especially under wet conditions. The extent of skid resistance available on any given pavement is dependent on the design of the surface texture. When the pavement surface texture is worn, the tire-pavement friction decreases and consequently the safety of the road users is reduced. Proper aggregate selection before construction helps in preserving adequate levels of skid resistance over the service life of the pavements. The critical engineering features expected from aggregates are their surface texture and resistance to the polishing actions of traffic. Currently, there are no standard quantitative test methods to measure the surface texture of aggregate particles directly. Besides, the existing laboratory test procedures used to evaluate the polishing resistance of aggregates are time-consuming, subjective, and cannot appropriately represent their frictional properties. A laboratory protocol is needed to assess the quality of aggregate particles for frictional purposes accurately.
Investigating the changes in pavement macrotexture is also required during the life of pavements to assess the potential skid-related problems and reduce crashes effectively. The pavement macrotexture is predominantly described by the Mean Texture Depth (MTD) using the Sand Patch Test (SPT), and by the Mean Profile Depth (MPD) using non-contact single-point profilometers. The reliability of the SPT is questionable because it is operator-dependent and the MPD calculated using a single texture profile cannot adequately describe the pavement texture. The limitations of the current measuring methods for pavement texture have directed researchers toward looking for new, more objective, accurate and reliable technologies that could potentially contribute towards better pavement surface texture evaluation. With a better texture characterization, highway agencies can better assess the level of skid resistance of their highway network with the aim of ensuring safer roads for the public. The main contributions of this dissertation include a series of procedures and algorithms, developed using a high-resolution line laser scanner (LLS) prototype, to improve capturing and characterizing hot-mix asphalt (HMA) pavement surface texture. The methods and algorithms that have been developed in this dissertation include: 1) An outlier and spikes removal technique and set of criteria employing several filters to create clean 3D models of pavement surfaces, 2) A methodology to obtain the micro- and macrotexture of the surface profiles captured by the LLS, and automatically calculate the MPD based on pavements 3D texture data, 3) An algorithm to mimic the SPT on pavements 3D models and to automatically estimate the MTD based on the 3D texture data, 4) A new approach for quantification and differentiation the aggregates surface texture by using spectral analysis, 5) Using the Micro-Deval machine along with the LLS to develop a method for measuring aggregate polishing resistance, and 6) To categorize the aggregate particles based on their polishing resistance using model-based clustering techniques. This study was complemented by conducting a series of experimental field tests to compare the performance of the developed macrotexture characterization algorithms with current test methods. Results confirmed that the proposed algorithms could provide efficient, reliable, repeatable, and accurate measurements of the pavement surface macrotexture. Laboratory experiments were also conducted to characterize the polishing properties of seven types of aggregates based on their texture evolution. Following the polishing process, surface elevation profiles were obtained using the LLS and analyzed to determine the rate of change in the surface texture of aggregate samples. Texture measurements were carried out by using several texture parameters. Both visual examinations and statistical analysis were used to identify the differences between the aggregates polishing properties. The results showed that the polishing tendency of aggregates evaluated could be grouped according to the rate of change in surface texture.

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