Comparison of Roadway Roughness Derived from LIDAR and SFM 3D Point Clouds, HR-3001, 2015

This report describes a short-term study undertaken to investigate the potential for using dense three-dimensional (3D) point clouds generated from light detection and ranging (LIDAR) and photogrammetry to assess roadway roughness. Spatially continuous roughness maps have potential for the identification of localized roughness features, which would be a significant improvement over traditional profiling methods. This report specifically illustrates the use of terrestrial laser scanning (TLS) and photogrammetry using a process known as structure from motion (SFM) to acquire point clouds and illustrates the use of these point clouds in evaluating road roughness. Five roadway sections were chosen for scanning and testing: three gravel road sections, one portland cement concrete (PCC) section, and one asphalt concrete (AC) section. To compare clouds obtained from terrestrial laser scanning and photogrammetry, the coordinates of the clouds for the same section on the same date were matched using open source computer code. The research indicates that the technologies described are very promising for evaluating road roughness. The major advantage of both technologies is the large amount of data collected, which allows the evaluation of the full surface. Additional research is needed to further develop the use of dense 3D point clouds for roadway assessment.

Impact of Curling and Warping on Concrete Pavement, TR-668, 2016

Portland cement concrete (PCC) pavement undergoes repeated environmental load-related deflection resulting from temperature and moisture variations across the pavement depth. This phenomenon, referred to as PCC pavement curling and warping, has been known and studied since the mid-1920s. Slab curvature can be further magnified under repeated traffic loads and may ultimately lead to fatigue failures, including top-down and bottom-up transverse, longitudinal, and corner cracking. It is therefore important to measure the “true” degree of curling and warping in PCC pavements, not only for quality control (QC) and quality assurance (QA) purposes, but also to achieve a better understanding of its relationship to long-term pavement performance. In order to better understand the curling and warping behavior of PCC pavements in Iowa and provide recommendations to mitigate curling and warping deflections, field investigations were performed at six existing sites during the late fall of 2015. These sites included PCC pavements with various ages, slab shapes, mix design aspects, and environmental conditions during construction. A stationary light detection and ranging (LiDAR) device was used to scan the slab surfaces. The degree of curling and warping along the longitudinal, transverse, and diagonal directions was calculated for the selected slabs based on the point clouds acquired using LiDAR. The results and findings are correlated to variations in pavement performance, mix design, pavement design, and construction details at each site. Recommendations regarding how to minimize curling and warping are provided based on a literature review and this field study. Some examples of using point cloud data to build three-dimensional (3D) models of the overall curvature of the slab shape are presented to show the feasibility of using this 3D analysis method for curling and warping analysis.