The Weighted Longitudinal Profile

Ueckermann, Andreas; Steinauer, Bernhard

doi:10.1080/14680629.2008.9690111

Cited by 9 publications

(7 citation statements)

References 7 publications

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“…Areas of high unevenness which occur not directly along the travel path of the RSE are not detected. Thus, more advanced methods such as 2D and 3D laser scanning and wavelet analysis [22,[26][27][28] are being researched. In Germany, non-contact methods are based on the high speed road monitoring method (HRM), in which four laser distance sensors are used in a linear arrangement for the long-and short-distance road excitations [9].…”

Section: Road Evenness Evaluation Based On Mobile Lidar Datamentioning

confidence: 99%

“…In the following, the weighted longitudinal profile (WLP) [7,22], a modern evaluation approach for the analysis of longitudinal evenness is presented. This approach will be introduced into regulations and standards for German and European levels.…”

Section: Road Evenness Evaluation Based On Mobile Lidar Datamentioning

confidence: 99%

“…The measurement vehicle can record both the base layer of the road before paving and also the surface of the finished road. This data can be used for two purposes: First, the surface scan of the finished road provides a 3D evenness map over the whole road instead of a locally limited 2D profile as provided by RSE or profilograph and more advanced evaluation criteria like the WLP [7,22]. This leads to an improved surface evaluation and can be used to identify and post-process areas with a large unevenness.…”

Section: Introductionmentioning

confidence: 99%

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Process Evaluation for Smart Concrete Road Construction:Road Surface and Thickness Evaluation Using High-Speed LiDAR Technology

Skalecki

Sesselmann²,

Rechkemmer

et al. 2021

Automation

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The enhancement of new quality criteria in highway construction is a key aspect to improving the construction process and lifetime of road. In particular, mobile laser scanning systems are nowadays able to provide realistic 3D elevation profiles of a road to detect anomalies. In this context, this study utilizes a high-accuracy high-speed mobile mapping vehicle and evaluates a weighted longitudinal profile as an improved measure for evenness analysis. For comparison a classical method with a rolling straight edge was evaluated on the same road section and observed effects are discussed. The second focus is the areal reconstruction of the road thickness. For this purpose, a modern method was developed to spatially synchronize two high-speed laser scans using reference boxes next to the road, to transfer the point clouds into a surface model and to calculate the layer thickness. This procedure was conceptually validated by some pointwise measurements of the layer thickness. With this information, imperfections in the base layer could be detected automatically over a wide area at an early stage and countermeasures might be initiated before constructing the highway.

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Section: Road Evenness Evaluation Based On Mobile Lidar Datamentioning

confidence: 99%

Section: Road Evenness Evaluation Based On Mobile Lidar Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Process Evaluation for Smart Concrete Road Construction:Road Surface and Thickness Evaluation Using High-Speed LiDAR Technology

Skalecki

Sesselmann²,

Rechkemmer

et al. 2021

Automation

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show abstract

“…The two approaches result in slightly different results; thus, any approach other than the FD solution described in ASTM 1926‐08 () will be referred to as roughness index (RI). The approaches described to calculate IRI values do not provide sufficient detail to identify localized features (Herabat and Songchitruksa, ; Ueckermann and Steinauer, ; Nitsche et al., ).…”

Section: Introductionmentioning

confidence: 99%

“…The two approaches result in slightly different results; thus, any approach other than the FD solution described in ASTM 1926ASTM -08 (2008 will be referred to as roughness index (RI). The approaches described to calculate IRI values do not provide sufficient detail to identify localized features (Herabat and Songchitruksa, 2003;Ueckermann and Steinauer, 2008;Nitsche et al, 2014). Quantifying localized features is crucial because they affect human comfort and cause vehicle fatigue damage (Steinwolf et al, 2002;Oijer and Edlund, 2004;Bogsjö and Rychlik, 2009).…”

Section: Introductionmentioning

confidence: 99%

Wavelet Filter Design for Pavement Roughness Analysis

Alhasan

White

Brabanter

2016

Computer aided Civil Eng

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Control and characterization of pavement roughness is a major quality assurance requirement. With emerging technologies in real‐time monitoring and increasingly stringent requirements to minimize localized roughness features, there is an opportunity to improve upon the traditional quarter‐car (QC) algorithm used to qualify roughness. Current methods suffer from phase lag that mislocates roughness features and require relatively long profiles to achieve high accuracy. In this study, continuous and discrete wavelet bases were modified in the frequency domain to design 116 new QC‐wavelet filters in the spatial domain that were used to analyze 30 road profiles. QC‐wavelet filters were compared to the currently used finite difference algorithm and filtering in the frequency domain. QC‐wavelet filters design based on a Daubechies and nonanalytic Morlet (i.e., db21 and morl0) wavelets outperformed the other filters and algorithms in terms of characterizing overall profiles and accurately quantifying localized features. The major advantages of the new approach include accurately estimating the position and severity of localized feature, and accurately analyzing short profile segments (i.e., <7.62 m).

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