On laser scanning, pavement surface roughness and international roughness index in highway construction

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F16%3A00243288" target="_blank" >RIV/68407700:21110/16:00243288 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.14311/EE.2016.294" target="_blank" >http://dx.doi.org/10.14311/EE.2016.294</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.14311/EE.2016.294" target="_blank" >10.14311/EE.2016.294</a>

Jazyk výsledku

angličtina

Název v původním jazyce

On laser scanning, pavement surface roughness and international roughness index in highway construction

Popis výsledku v původním jazyce

The paper reports data from the research project where the objective was to develop and validate a tool that would be publicly available and would leverage the point cloud data commonly acquired on sites to calculate the pavement surface properties such as the International Roughness Index and Roughness. To do so, a unique RIRI program was written in Python to streamline the point cloud data analysis. The program is publicly available under the GNU General Public License. Further, the paper presents data from three test sections where the developed methodology was used to calculate the pavement smoothness properties from a point cloud and compared to classical, reference, methodologies, such as the rod and level and precise levelling. The paper focuses on the variability and precision of all methodologies. It was found that the Pearson type IV distribution is a fitting descriptor for histograms calculated with the help of Freedman and Diaconis’s law from rectified slopes and roughness values with regard to its fitness and use of its parameters for the pavement surface smoothness description.

Název v anglickém jazyce

On laser scanning, pavement surface roughness and international roughness index in highway construction

Popis výsledku anglicky

The paper reports data from the research project where the objective was to develop and validate a tool that would be publicly available and would leverage the point cloud data commonly acquired on sites to calculate the pavement surface properties such as the International Roughness Index and Roughness. To do so, a unique RIRI program was written in Python to streamline the point cloud data analysis. The program is publicly available under the GNU General Public License. Further, the paper presents data from three test sections where the developed methodology was used to calculate the pavement smoothness properties from a point cloud and compared to classical, reference, methodologies, such as the rod and level and precise levelling. The paper focuses on the variability and precision of all methodologies. It was found that the Pearson type IV distribution is a fitting descriptor for histograms calculated with the help of Freedman and Diaconis’s law from rectified slopes and roughness values with regard to its fitness and use of its parameters for the pavement surface smoothness description.

Druh

D - Stať ve sborníku

CEP obor

JN - Stavebnictví

OECD FORD obor

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Projekt

<a href="/cs/project/TE01020168" target="_blank" >TE01020168: Centrum pro efektivní a udržitelnou dopravní infrastrukturu (CESTI)</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of 6th Eurasphalt & Eurobitume Congress,1st - 3rd June 2016

ISBN

978-80-01-05962-3

ISSN

—

e-ISSN

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Počet stran výsledku

3

Strana od-do

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Název nakladatele

Czech Technical University

Místo vydání

Prague

Místo konání akce

Praha

Datum konání akce

1. 6. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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