Models for road surface roughness

Bogsjö, Klas; Podgórski, Krzysztof; Rychlik, Igor

doi:10.1080/00423114.2011.637566

Cited by 85 publications

(75 citation statements)

References 21 publications

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“…This recommendation was followed by many researchers (Marchesiello et al 1999;Bogsjö et al 2010;Coussy et al 1989;Kamash and Robson 1978;Uyls et al 2007). In a more detailed analysis, (Captain et al 1979;Chang et al 2011;Camara et al 2014) employed a disk model with a rigid tread band that considers explicitly the wheel dimensions and its filtering effects.…”

Section: Introductionmentioning

confidence: 99%

Influence of the pavement surface on the vibrations induced by heavy traffic in road bridges

et al. 2017

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Section: Introductionmentioning

confidence: 99%

Influence of the pavement surface on the vibrations induced by heavy traffic in road bridges

et al. 2017

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“…Snow sastrugi may also introduce erroneous surface roughness values that are not associated with the sea ice topography. Quantifying surface roughness in the natural environment remains a challenge, with the most widely used approach being the standard deviation of relative surface elevation; however other statistical parameters such as skewness and kurtosis have been proposed [42]. Furthermore, roughness measurements are highly scale-dependent and large-scale variations in topography may introduce uniform and along flight line errors.…”

Section: Discussionmentioning

confidence: 99%

Linking Regional Winter Sea Ice Thickness and Surface Roughness to Spring Melt Pond Fraction on Landfast Arctic Sea Ice

et al. 2017

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Abstract:The Arctic sea ice cover has decreased strongly in extent, thickness, volume and age in recent decades. The melt season presents a significant challenge for sea ice forecasting due to uncertainty associated with the role of surface melt ponds in ice decay at regional scales. This study quantifies the relationships of spring melt pond fraction (f p ) with both winter sea ice roughness and thickness, for landfast first-year sea ice (FYI) and multiyear sea ice (MYI). In 2015, airborne measurements of winter sea ice thickness and roughness, as well as high-resolution optical data of melt pond covered sea ice, were collected along two~5.2 km long profiles over FYI-and MYI-dominated regions in the Canadian Arctic. Statistics of winter sea ice thickness and roughness were compared to spring f p using three data aggregation approaches, termed object and hybrid-object (based on image segments), and regularly spaced grid-cells. The hybrid-based aggregation approach showed strongest associations because it considers the morphology of the ice as well as footprints of the sensors used to measure winter sea ice thickness and roughness. Using the hybrid-based data aggregation approach it was found that winter sea ice thickness and roughness are related to spring f p . A stronger negative correlation was observed between FYI thickness and f p (Spearman r s = −0.85) compared to FYI roughness and f p (r s = −0.52). The association between MYI thickness and f p was also negative (r s = −0.56), whereas there was no association between MYI roughness and f p . 47% of spring f p variation for FYI and MYI can be explained by mean thickness. Thin sea ice is characterized by low surface roughness allowing for widespread ponding in the spring (high f p ) whereas thick sea ice has undergone dynamic thickening and roughening with topographic features constraining melt water into deeper channels (low f p ). This work provides an important contribution towards the parameterizations of f p in seasonal and long-term prediction models by quantifying linkages between winter sea ice thickness and roughness, and spring f p .

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“…Simply speaking it is Gaussian processes where the variance is randomly changing. In the current paper we present Laplace modelling based on (Bogsjö et al, 2012;Johannesson & Rychlik, 2014) and its extensions and applications presented in (Johannesson & Rychlik, 2013;Kvanström et al, 2013;Johannesson et al, 2015a;Kozubowski et al, 2013).…”

Section: Road Roughness Modellingmentioning

confidence: 99%

“…filtered (smoothed) Gaussian noise X k . In (Bogsjö et al, 2012) it was shown that replacing Gaussian noise by Laplace noise √ R k X k produces a more accurate model of roughness. In this paper we summarize principles of Laplace distribution modelling that arise from work in (Åberg et al, 2009;Bogsjö et al, 2012) and its extensions and applications presented in (Johannesson & Rychlik, 2013, 2014Kvanström et al, 2013;Johannesson et al, 2015a;Kozubowski et al, 2013).…”

Section: Roughnessmentioning

confidence: 99%