Evaluation of pavement skid resistance using high speed texture measurement

Meegoda, Jay N.; Gao, Shengyan

doi:10.1016/j.jtte.2015.09.001

Cited by 45 publications

(27 citation statements)

References 6 publications

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“…Several studies indicate that these functional characteristics are highly associated with pavement texture properties (Ergun and Agar, 2010;Flintsch et al, Li and Wang, 2016;Torbruegge and Wies, 2015;Meegoda and Gao, 2015). Pavement surface texture is defined as the deviation of the pavement surface from a true planar surface or an ideal shape (ASTM E867, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…Skid resistance on a road surface is affected by both micro-texture and macro-texture (Li and Wang, 2016;Torbruegge and Wies, 2015;Meegoda and Gao, 2015;Hall et al, 2006), and wet pavement friction is primarily affected by macro-texture (Ergun and Agar, 2010;Luo et al, 2014). In the past decades, several indicators have been proposed and used to characterize the pavement surface texture, such as Mean Profile Depth (MPD), Mean Texture Depth (MTD), Hessian Model, and Power Spectral Density (PSD) (Wang et al, 2012;Abbas et al, ASTM E2157-15, 2015ASTM E1845-15, 2015ASTM E965-15, 2015;Luo et al, 2016;Gendy and Shalaby, 2007;Sayers and Karamihas, 1996; ASME B46.1, 2009).…”

Section: Introductionmentioning

confidence: 99%

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Impacts of Sample Size on Calculation of Pavement Texture Indicators with 1mm 3D Surface Data

Wang

et al. 2017

Period. Polytech. Transp. Eng.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Impacts of Sample Size on Calculation of Pavement Texture Indicators with 1mm 3D Surface Data

Wang

et al. 2017

Period. Polytech. Transp. Eng.

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“…In the field, skid resistance was measured using LWT with a smooth tire at 65 km/h. Rajaei et al (2014) In a more recent study, Meegoda and Gao (2015) Where SNR40 = skid number measured with LWT with ribbed tire at 40 mph (64 km/h), and MPD = macrotexture in mm.…”

Section: Estimating Friction From Pavement Texturementioning

confidence: 99%

“…The poor relationship between SN64R and MPD indicates that only 1% of SN64R variation may be explained by MPD. Previous studies have shown that skid resistance and macrotexture were correlated with R-squares that ranged from 0.29 to 0.83, depending on the type of data and devices used for data collection (Ahammed and Tighe, 2012;Rajaei et al, 2014;Serigos et al, 2014;Meegoda and Gao, 2015). It is possible the poor correlation between SN64R and MPD was influenced by several factors, including problems with data collection, device calibration, errors in the LWT tests, environmental conditions, and contaminants on the pavement surface (e.g., dust, debris), among other things.…”

Section: Relationship Between Skid Resistance and Macrotexturementioning

confidence: 99%

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Investigation of the Influence of the Condition of Asphalt Pavement Surface on Road Safety of Rural Ontario Highways

Omar¹

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Road collisions are complex events that are influenced by a combination of factors, including driver behaviour, environmental condition (e.g., icy and wet roads), road geometry, roadside elements, vehicle speed, tire deficiencies, traffic, and pavement condition. While the influence of some of these factors has been studied extensively for decades, the influence of pavement condition on road safety is relatively underresearched. This research investigated the influence of pavement surface condition on road safety by developing statistical models that correlate pavement surface condition and collisions. This research also examined the possibility to integrate skid resistance into pavement management by investigating the correlations between skid resistance, pavement distress, and operational conditions of the roads. This study was limited to rural arterial and freeways of the Ontario asphalt pavement road network. Data of pavement condition, operational condition, and collision was obtained from the Ontario Ministry of Transportation for 6879 kilometers across 37 provincial rural highways for the period of 2012 to 2014. Pavement condition data was collected at network level with an automatic road analyzer road and included information about roughness, rutting, cracking, and macrotexture. Skid resistance data was collected with a locked wheel tester. The collected data was combined into a spatial data model, also known as a vector-based geographic information system. The results of the investigation using regression analysis showed that pavement friction is affected by traffic, pavement age, and pavement distress. Skid resistance decreased with the increase of traffic and increased with the increase of pavement distress. Macrotexture increased with the increase of traffic and pavement distress. 1 Chapter: Introduction This chapter presents background information about the research problem statement, gaps in the literature related to pavement condition and road safety, objectives, and scope of this study. This chapter also outlines the research plan and thesis organization. 1.1 Background Canada is a large country with rural regions and urban centres separated by great distances. Highways and roads play a crucial role in delivering numerous social and economic benefits, including: integrating communities, facilitating access to education and health care, increasing mobility of people and goods, reducing transport costs, improving access to markets, increasing business and industrial opportunities, and increasing employment opportunities generated by the construction and maintenance of roads (Burningham and Stankevich, 2005). However, in order for these benefits to be sustainable, the serviceability of roadway pavements must be preserved. Serviceability considers that roadways are built for the comfort, convenience, and safety of road users

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Use of Advanced Techniques for Functional Evaluation of Pavements: A Review and a Pilot Study

Kumar

2023

Lecture Notes in Civil Engineering

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Evaluation of pavement skid resistance using high speed texture measurement

Cited by 45 publications

References 6 publications

Impacts of Sample Size on Calculation of Pavement Texture Indicators with 1mm 3D Surface Data

Impacts of Sample Size on Calculation of Pavement Texture Indicators with 1mm 3D Surface Data

Investigation of the Influence of the Condition of Asphalt Pavement Surface on Road Safety of Rural Ontario Highways

Use of Advanced Techniques for Functional Evaluation of Pavements: A Review and a Pilot Study

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