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Quantification of skid resistance seasonal variation in asphalt pavements
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Original Research Paper
Quantification of skid resistance seasonal variation in asphalt pavements Christina Plati*, Maria Pomoni, Konstantina Georgouli Laboratory of Pavement Engineering, School of Civil Engineering, National Technical University of Athens (NTUA), Athens 15773, Greece
highlights Quantification of skid resistance seasonal variation alerts for proactive actions. Distribution fitting enables the estimation of a seasonal variation change factor. Burr e Dagum e generalized extreme value accommodate skid resistance data. Change factor is developed for an urban highway and validated for an interurban one. Seasonal variation change factor may be dependent on the asphalt mix type.
article info
abstract
Article history:
The present study deals with the quantification of skid resistance seasonal variation in
Received 15 April 2018
asphalt pavements. Consequently, skid resistance data were collected twice per year (after
Received in revised form
“wet” and “dry” seasons) along four trial sections of an urban highway with a GripTester
6 July 2018
system (during a six-year field experiment). For the estimation of a percentage reference
Accepted 16 July 2018
change (%) between wet and dry skid resistance data, a methodology is developed based on
Available online xxx
distribution fitting of the percentage change of skid resistance index due to seasonal variation impact. The outcome of the analysis is applied and successfully validated
Keywords:
through a set of skid resistance data collected from an interurban highway pavement
Asphalt pavement
section with different traffic and environmental conditions. As such the developed
Skid resistance
methodology can be considered as a promising tool for accommodating the seasonal
Seasonal variation
variation of skid resistance. Further, the resulting percentage reference change (%) enables
Quantification
the estimation of skid resistance level for the dry period based on the wet period mea-
Road safety
surements. Overall, it seems that the proposed quantification process may constitute a practical yet efficient tool for road authorities and highway operators on how to estimate the effect of seasonal variation on skid resistance levels and schedule well-ahead necessary actions for enhancing driving safety. © 2019 Periodical Offices of Chang'an University. Publishing services by Elsevier B.V. on behalf of Owner. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/).
* Corresponding author. Tel.: þ30 210 772 1363; fax: þ30 210 772 4254. E-mail addresses: [email protected] (C. Plati), [email protected] (M. Pomoni), [email protected] (K. Georgouli). Peer review under responsibility of Periodical Offices of Chang'an University. https://doi.org/10.1016/j.jtte.2018.07.003 2095-7564/© 2019 Periodical Offices of Chang'an University. Publishing services by Elsevier B.V. on behalf of Owner. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Please cite this article as: Plati, C et al., Quantification of skid resistance seasonal variation in asphalt pavements, Journal of Traffic and Transportation Engineering (English Edition), https://doi.org/10.1016/j.jtte.2018.07.003
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J. Traffic Transp. Eng. (Engl. Ed.) xxxx; xxx (xxx): xxx
1.
Introduction
1.1.
Background
The main function of a highway pavement is to serve the transportation of human beings and freights in a safe, comfortable and economic way by simultaneously protecting natural soil. This implies that a pavement structure needs to present increased serviceability condition. For that, pavements should be firstly structurally sufficient to effectively bear the increasingly demanding traffic loads under several environmental and climatic conditions. Thus, it is indispensable for road authorities to be aware of the importance of monitoring and evaluating asphalt pavements performance, with the view to improving pavements durability and road safety in conjunction with environmental and sustainable perspectives (Losa et al., 2008). Especially, pavements functional performance ensures the provision of a safe and comfortable ride to road users for a specific range of speeds. One of the most important factors that determine pavement functional performance in terms of safety is the level of friction of the wearing course (Meegoda and Gao, 2015). Safe driving depends on adequate surface friction for vehicle maneuvering, turning and braking. However, in case of a slippery road, a necessary emergency maneuvering due to skidding could cause loss of control or even a collision (Flintsch et al., 2012). It has been estimated that approximately 35% of crashes are caused at least partially because of skidding, while an improvement of 10% in the average level of skid resistance reduces skidding accident rate by 13% (Rodriguez, 2009). Skid resistance is defined as the resistive force developed when a tire is prevented from rotating along the pavement surface (AASHTO, 2008; Kogbara et al., 2016; Plati et al., 2014). Pavement surface skid resistance mainly refers to the tirepavement interaction and the resulting of that interaction, friction force (Kogbara et al., 2016). Pavement friction mainly consists of two basic components: adhesion and hysteresis (AUSTROADS, 2005; Kane et al., 2017). Adhesion results from the formation of molecular bonds between the tire tread and the pavement surface (Transportation Safety Board of Canada, 2010; Villani et al., 2011). It represents the shear force that is created when the tire tread changes its shape in order to conform to the contacting asperities of the surface aggregates (Wilson, 2006). As such, microtexture of the aggregates influences the adhesion component (Prowell et al., 2003). Hysteresis is developed due to the continuous compression and decompression phases that the tire-tread experiences when it comes in contact with the aggregates. These continuous phases cause loss of energy in form of heat (Hall et al., 2009; Wilson, 2006). Consequently, hysteresis is affected by the overall form of the aggregates, indicating that macrotexture influences this component. Practically, the aforementioned components could be more determinant in case a vehicle starts braking and slides e skids on a pavement surface, thus the aggregates of the pavement surface are in contact with the tire tread having a crucial role in immobilizing the vehicle. For the case of a dry and smooth surface, adhesion is predominant as the contacting asperities
contribute to the total friction force that helps vehicle to immobilize (Flintsch et al., 2003; Wilson, 2006). On the other hand, if the surface is wet, macrotexture is necessary to accelerate the drainage of water from the tire-pavement interface so as to prevent vehicles tires from aquaplaning (Wilson, 2013). However, the faster the vehicle moves away, the less the asperities penetrate the tire tread, consequently the adhesion component is not available as well as hysteresis loss cannot contribute to friction (Rogers and Gargett, 1991). Great efforts have been made worldwide to investigate the above friction components through advanced numerical modeling of tire-pavement interaction. Finite element method (FEM) has been widely used towards this goal (Srirangam et al., 2015, 2017). Anupam et al. (2013) have made a parametric study to incorporate the effect of several factors affecting hysteric friction of a tire slipping over an asphalt pavement surface, including pavement temperature, ambient temperature, different macrotextures profiles or tire slip ratios. Do et al. (2013) emphasized the strong correlation between friction and the number of contacting road asperities pinpointing the water-depth dependency of friction. They observed that when the water depth nearly covers the contacting asperities, their number and their height are rather essential. Profoundly, asphalt mixes characteristics of pavement wearing courses significantly affect pavements skid resistance performance and variation. Several material engineering aspects have been widely investigated in numerous relevant research studies such as the physical and shape characteristics of aggregates (Araujo et al., 2015; Bessa et al., and Vaiana, 2015; Rezaei et al., 2014; Masad et al., 2010; Pratico 2011; Vaiana et al., 2012). Also among them, the aggregate polishing agents, the affinity between aggregate and bitumen and the wetness conditions constitute valuable quantitative and qualitative information for field or laboratory skid resistance assessment or even for the development of surface friction predictive models (Wang et al., 2013). To that extent, the input of traffic data is also important. Particularly, the type of vehicles that move on road surfaces is believed to greatly influence the skid resistance level (Khasawneh et al., 2015). Recent studies have shown that the skid resistance degree is more closely affected by the number of duty vehicles per lane per day, rather than the total number of all vehicles (Mulry et al., 2012; Ragland et al., 2010). Also, the utilized measuring device may affect the skid resistance degree due to the differences in measuring principles, this highlights the importance for the harmonization of pavement friction measuring systems (Kogbara et al., 2016). From a more practical engineering perspective, numerous studies (Ahammed and Tighe, 2009; Bijsterveld and Del Val, 2015; Fwa, 2017; Hill and Henry, 1981; Oliver et al., 1988; Prowell et al., 2003) have focused on skid resistance seasonal variation in the short-term (i.e., throughout a year), its contributing factors and challenges related to its quantification. Initially, once the pavement is put in service, an increase in skid resistance is expected as the film of asphalt bitumen of the surface aggregates is worn away by the traffic flow and hence, macrotexture and microtexture components govern. Then, once the aggregates are exposed without been covered
Please cite this article as: Plati, C et al., Quantification of skid resistance seasonal variation in asphalt pavements, Journal of Traffic and Transportation Engineering (English Edition), https://doi.org/10.1016/j.jtte.2018.07.003
J. Traffic Transp. Eng. (Engl. Ed.) xxxx; xxx (xxx): xxx
by asphalt bitumen, given a constant traffic flow, any fluctuations in skid resistance mainly occur because of the seasonal and/or short-term environmental variations (Wilson, 2006). Also, it seems that the amount of the asperities of aggregates used in the asphalt-mixture (microtexture) can also influence the polishing behavior of an asphalt road surface and consequently affect pavement functional performance in terms of skid resistance (Georgiou and Loizos, 2014; Kane et al., 2012). Actually, the major process that prevails and affects seasonal variation has not yet been clearly understood (Ahammed and Tighe, 2009; Hall et al., 2009). Generally, a typical pavement surface presents different performance in terms of skid resistance levels during wet and dry seasons. The main hypothesis is that during dry summer months, skid resistance is lower as vehicle tires cause disintegrated debris to continuously ground up, producing fine dust which subsequently extends the polishing of the surface aggregates. On the other hand, during the wet winter months loose debris is easily cleansed, under the influence of rainfall and traffic, acting against the fine dust formation. In that case, coarse debris is more predominant and under the movement of vehicle tires, pavement surface becomes rougher resulting in higher skid resistance levels (Dunford and Roe, 2010; Do et al., 2007; Donbavand and Cook, 2005). Temperature is listed as a factor affecting the available pavement skid resistance (Xie et al., 2018). However, modern rubber compounds are formulated to remove the effect of temperature in friction under normal running speeds and temperatures. Typically, friction is reduced at high temperatures where the rubber becomes soft because hysteresis is affected by the viscoelastic property of the tire. Also the lower the temperature, the faster the friction decreases (Xie et al., 2018). These higher temperatures are typically reached when a lot of breaking is performed (Anupam et al., 2013). In view of the above, it becomes obvious that accurate quantification of skid resistance seasonal variation is a rather complex task. As such, it has not been yet possible to jointly consider the potential impact of different affecting factors on skid resistance seasonal evolution, as it is difficult to fully incorporate the influence caused to skid resistance seasonal variation by each one of them (Bijsterveld and Del Val, 2015) and univocally apply it in various road conditions. However, considering that highway agencies propose recommendations for safety against skidding based either on past experience or on skid resistance measurements (Anupam et al., 2013), the assessment and interpretation of any field measurements needs probably to be strictly aligned with the season of year they are undertaken due to the seasonal variations effects (Ragland et al., 2010). Towards this direction, specific standards exist (Williamsleatag, 2015), adopting the terminology wet and dry skid resistance measurements and recommend skid resistance levels to be measured under standardized conditions. This should be accomplished during the periods where precipitation occurrence is less frequent (i.e., dry seasons) and the lowest measured skid resistance values are observed. However, as a common practice skid resistance measurements are undertaken more than once per year (after wet and dry
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seasons) in order to capture the seasonal variation effect on skid resistance evolution. As such, it still remains a big challenge for road authorities and pavement engineers to develop methodological approaches or even establish a process for the quantification of skid resistance seasonal variation in asphalt pavements so as to organize, if needed, any preventive actions well-ahead.
1.2.
Objective
The motivation of the current research study lies upon the fact that by performing skid resistance measurements during the wet season (after the period of intense precipitation) on the one hand, and having knowledge of the evolution of skid resistance due to seasonal variations on the other hand, a rough, yet reliable estimation of the expected skid resistance level during the dry seasons will be feasible. In case of potentially low expected levels during the dry season, appropriate intervention actions will have to be scheduled wellahead, in favor of road safety. Thus, quantifying skid resistance seasonal variation in asphalt pavements may constitute a useful and practical approach towards enhancing the knowledge of its evolution within a year. A previous preliminary study conducted by Plati et al. (2014) focused on the aforementioned quantification through a simplified approach. On these grounds, the current research aims to investigate a relative change factor for quantifying skid resistance seasonal variation that occurs within a year, concentrating on the rainfall effect, so as to assist road authorities and highway operators on decision making of maintenance activities with a view to enhancing driving safety. Towards this, skid resistance data were collected twice per year (after wet and dry seasons) from four trial sections of an urban highway with a GripTester system during a six-year field experiment (Findlay Irvine Ltd, 2002). For the estimation of a percentage reference change (%) between wet and dry skid resistance data, a methodology is developed based on distribution fitting of the percentage change of skid resistance index due to seasonal variation impact. The outcome of the analysis is validated through a set of skid resistance data collected from an interurban highway, as described in the following.
2.
Methodology
2.1.
Test sites and field experiment
It has been already mentioned that skid resistance data were collected from four trial sections (S1eS4) of an urban highway over a six-year period (Y1eY6). Experimental measurements were undertaken along the outer wheel path of the right lane, utilizing a GripTester system (Choi, 2011; Findlay Irvine Ltd, 2002). The GripTester is a fixed-slip device that can be used for effectively monitoring the surface of highways' pavements. The device consists of a three-wheeled system and a smoothtread tire is fitted to the central wheel, utilized for skid resistance measurements, according to ASTM E1844 standard (ASTM, 2015). The axle of the test wheel is connected to a chain-system that controls the test wheel's slip speed by
Please cite this article as: Plati, C et al., Quantification of skid resistance seasonal variation in asphalt pavements, Journal of Traffic and Transportation Engineering (English Edition), https://doi.org/10.1016/j.jtte.2018.07.003
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J. Traffic Transp. Eng. (Engl. Ed.) xxxx; xxx (xxx): xxx
providing a constant slip ratio of 14%. Also, a water-pump of the GripTester ensures a constant 0.5 mm water-film supply on the pavement surface, allowing skid resistance measurements at a certainly wet pavement surface. The system reports an average index of skid resistance measurements, called the grip number (GN), which is averaged at 10 m intervals for this study. In general, GN index is dimensionless and ranges from 0 to 1. Increased skid resistance is expected for higher GN values. Regarding the asphalt mixtures characteristics of the pavement sections being investigated, they are of hot mix asphalt concrete, surfaced with an antiskid surface layer, with an open mixture type corresponding to the O-5 mix designation, as defined in ASTM standards (D-3515). Steel slag was utilized in the mix design as an aggregate and the mix produced utilizing a 25-55/70 asphalt grade penetration modified with polymer modified bitumen with a binder content of 4.0% by mass of the mixture, while the air void content of the asphalt mix was 11.5% by mass of the mixture (Plati et al., 2014). It is worthwhile mentioning that the sections under investigation were constructed at different time periods with the latest to have been constructed three years after the older one. The length of the sections is between 10 and 50 km. Based on the extensive routine pavement monitoring all the pavements were structurally sound without any indication of rutting, roughness problems, or hydroplaning. Skid resistance measurements were conducted shortly after a 5-month period of precipitation (wet season) and after a 5-month period of low precipitation occurrence and higher
temperatures (dry season). Also air temperature was recorded, ranging from 2 C to 15 C for wet seasons and from 25 C to 35 C for dry seasons. As it has been already mentioned, for validation purposes, skid resistance measurements were also performed along another pavement section of an interurban highway over a period of one year. The pavement age of the interurban highway pavement section was 3 years, while its length is about 20 km. Measuring principles as well as the type of asphalt wearing course were the same. However, climatic conditions and traffic volume differed significantly. Air temperature during the measurements ranged from 4 C to 12 C in the wet season, while in the dry season, temperature ranged from 20 C to 25 C. It is also noted that the climate in the area of the interurban highway pavement section is rather humid with considerable rainfall occurrence during the year. Additionally, traffic volume is less intense than in the urban highway considering relevant information of the vehicle flow of both duty vehicles and passengers cars.
2.2.
Collected data
According to collected skid resistance data from the urban pavement sections, noticeable differences exist between GN values measured in wet seasons (GNW) and those measured in dry seasons (GND) during each year. Box plots in Figs. 1e4 illustrate the range of the collected data at the four trial sections (S1eS4) during the six-year period (Y1eY6). The central line in the box indicates the average value of the data sample, given that the coefficients of variation for each case are at a low level (less than 20%). The upper and the lower hinges of
Fig. 1 e Measured GN values from S1. (a) GNW. (b) GND.
Fig. 2 e Measured GN values from S2. (a) GNW. (b) GND. Please cite this article as: Plati, C et al., Quantification of skid resistance seasonal variation in asphalt pavements, Journal of Traffic and Transportation Engineering (English Edition), https://doi.org/10.1016/j.jtte.2018.07.003
J. Traffic Transp. Eng. (Engl. Ed.) xxxx; xxx (xxx): xxx
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Fig. 3 e Measured GN values from S3. (a) GNW. (b) GND.
Fig. 4 e Measured GN values from S4. (a) GNW. (b) GND.
the box indicate the 75% and the 25% percentile respectively of the data sample. Finally, the whiskers (the ends of the vertical lines) indicate the minimum and maximum values of each data sample. As expected, the average GNW values are clearly higher than the corresponding GND ones for all sections. GNW generally range from 0.41 to 0.65. In these urban highway sections, GNW values are generally satisfactory owing to winter rainfalls. However, the combined effect of the traffic volume in conjunction to the presence of contaminants on the pavement surface that cannot be totally moved away by the wet season rainfalls, leads to poor skidding pavement performance during the dry season, reflected through low GND values. Afterwards, it can be observed that the data sets of S1 present greater variance than the other sections for each year. This might happen due to remarkable deviations in the traffic flow level within S1. As such, data sets will be further analyzed in the following chapter so as to investigate their appropriateness for being included in the development of the quantification process. As far as the interurban highway section is concerned, Fig. 5 illustrates the average GN values from the data collected during the year of measurements (3rd year of in-service period) for both wet and dry season (GNW and GND). It is worthwhile mentioning that for this interurban highway section the average GNW value is 0.74 while the GND one is 0.53. According to this, it should be stated that the degradation in the interurban highway skid resistance level during dry season remains in a satisfactory level of GND (that of 0.53 GN). This is more likely to happen because of climatic
conditions in the geographic area of the interurban highway that can be rainy during the dry (summer) period as well, as opposed to the area of the urban highway.
2.3.
Data analysis
Upon quantifying the seasonal variation effect in GN values recorded from the urban highway pavement sections, a percentage change between GNW and GND values eDGNij (%) for the i-section and j-year is determined according to Eq. (1). DGNij ¼
GND; ij GNW;ij $100% GNW;ij
(1)
where 1 i 4, 1 j 6. The adjustment of appropriate distributions at each set of DGNij data is investigated. The suitability and adaptability of
Fig. 5 e Measured GN values from the interurban highway pavement section (wet and dry).
Please cite this article as: Plati, C et al., Quantification of skid resistance seasonal variation in asphalt pavements, Journal of Traffic and Transportation Engineering (English Edition), https://doi.org/10.1016/j.jtte.2018.07.003
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each distribution is controlled through the following tests for goodness of fit: the KolmogoroveSmirnov test and the Anderson-Darling test. The KolmogoroveSmirnov test for the estimation of goodness of fit is based on comparing an empirical distribution function with the distribution of the hypothesized function. It can be applied only to continuous distributions with estimated parameters, acting conservatively. The Anderson-Darling test is a general test to compare the fit of an observed cumulative distribution function with an expected cumulative distribution function. This test gives more weight to the tails of a data sample than the KolmogoroveSmirnov test (D'Agostino and Stephens, 1986). The KolmogoroveSmirnov test is defined as Dn ¼ supjFnðxÞ EðxÞj
(2)
where Fn(x) is the actual distribution, E(x) is the hypothesized distribution. Also, the Anderson-Darling test is defined as A2 ¼ n
n 1X fð2i 1Þ½ln FðXi Þ þ lnð1 FðXniþ1 ÞÞg n i¼1
ak1 ak xg b f ðxÞ ¼ h a ikþ1 b 1 þ xg b
a1 ak xg b f ðxÞ ¼ h a ikþ1 xg b 1þ b
(4)
and the cumulative distribution function is defined as (5)
where k is a continuous shape parameter (k > 0), a is a continuous shape parameter (a > 0), b is a continuous scale
(6)
and the cumulative distribution function is defined as " FðxÞ ¼ 1 þ
a #k xg b
(7)
The probability density function of Gen. Extreme is defined as 8 i h > 1 11 1 > > < ð1 þ kzÞ k exp ð1 þ kzÞ k s f ðxÞ ¼ > 1 > > : exp½ z expðzÞ s
(3)
where F(Xi) is the actual distribution. Based on the continuous nature of pavement skid resistance data that constitutes identically distributed random values describing a physical event which occurs with small probability, three continuous distributions have been preliminarily found to be more suitable for this sort of data amongst numerous continuous distributions. These distributions include the Burr distribution described through four parameters e Burr (4P), the Dagum distribution described as well through four parameters e Dagum (4P) and finally the generalized extreme value distribution e Gen. Extreme (Kotz and Nadarajah, 2000). To best of the authors' knowledge, the present study constitutes a first investigation approach where these distributions are utilized to accommodate skid resistance data of asphalt pavements. Below, the mathematical background of these distributions is briefly presented. The probability density function of Burr (4P) is defined as
a k xg FðxÞ ¼ 1 1 þ b
parameter (b > 0), g is a continuous location parameter (g x < þ∞). The probability density function of Dagum (4P) is defined as
ks0 (8) k¼0
and the cumulative distribution function is defined as
FðxÞ ¼
8 i h > < exp ð1 þ kzÞ1k
ks0
> : exp½expðzÞ
k¼0
(9)
where z ≡ (xm)/s, s is a continuous scale parameter, m is a continuous location parameter.
3.
Results
3.1.
Distribution fitting
The considered distributions for each set of data corresponding to a specific section (S1eS4) of the investigated road network and analysis year (Y1eY6) are shown in Table 1. The term “non available” stands for the cases where none of the available distributions can be fitted to the respective data. The histograms as well as the distribution curves describing the input data are presented in Figs. 6 e 9. Tables 2e5 show the results of the KolmogoroveSmirnov test (Eq. (2)) and the Anderson-Darling test (Eq. (3)) for goodness of fit for each data sample. The most appropriate test for each case is considered with the view to better adapting the selected distribution. However, as it can be observed there are DGNij samples for which none distribution, from the utilized ones, could be fitted. One possible reason behind that could be the greater variations in those DGNij samples that made difficult the distribution fitting. In a way, this indicates that those samples may not
Table 1 e Distributions for DGNij (%) samples per year (j ¼ 1, 2, …, 6) and section (i ¼ 1, 2, 3, 4). i-section
S1 S2 S3 S4
j-year Y1
Y2
Y3
Y4
Y5
Y6
Non available Non available Burr (4P) Burr (4P)
Burr (4P) Gen. Extreme Gen. Extreme Gen. Extreme
Dagum (4P) Dagum (4P) Gen. Extreme Non available
Non available Dagum (4P) Gen. Extreme Dagum (4P)
Non available Burr (4P) Dagum (4P) Gen. Extreme
Non available Non available Gen. Extreme Gen. Extreme
Please cite this article as: Plati, C et al., Quantification of skid resistance seasonal variation in asphalt pavements, Journal of Traffic and Transportation Engineering (English Edition), https://doi.org/10.1016/j.jtte.2018.07.003
J. Traffic Transp. Eng. (Engl. Ed.) xxxx; xxx (xxx): xxx
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Fig. 6 e Distribution fitting of S1, DGN1j (j ¼ 2, 3). (a) Y2, distribution: Burr (4P). (b) Y3, distribution: Dagum (4P).
be considered as appropriate for further analysis, and as such the exclusion of them might be an acceptable approach. The significance level (a0 ) indicates the maximum fixed level that is considered for common statistic reasons, up to which the null hypothesis (Ho) can be accepted. Also for this level, a critical value was obtained from a standard table and if it was greater than the corresponding test statistic value, the assumed distribution was accepted for the data description.
3.2.
Development of a reference change factor
Based on the above, Table 6 summarizes the estimated representative DGNij, stemming from the distribution fitting at each section for every year of the investigation. Actually, those DGNij values present the maximum value of the probability density function, in other words those values are most likely to be observed (sampled) in each data set and hence, they are considered as representative.
In an attempt to come up with an overall relative reference percentage change (DGNref (%)) irrespective of the section under investigation or the analysis year, all DGNij values from Table 6 are considered to form a new data sample which is subjected to an additional statistical analysis. Herein descriptive statistical indicators, shown in Table 7, are used owing to the fact that the new data sample consists of few values. Towards establishing a safe approach and taking into consideration the above, a relative reference percentage change DGNref of 35% between wet and dry seasons for the particular urban highway sections is selected. Accordingly, an estimation of GND values can be suggested as following based on wet season e skid resistance measurements. GND ¼ 65%GNW
(10)
It might be argued that the information provided by the previous sample, albeit conservative, could constitute a useful
Fig. 7 e Distribution fitting of S2, DGN2j (j ¼ 2, 3, 4, 5). (a) Y2, distribution: Gen. Extreme. (b) Y3, distribution: Dagum (4P). (c) Y4, distribution: Dagum (4P). (d) Y5, distribution: Burr (4P). Please cite this article as: Plati, C et al., Quantification of skid resistance seasonal variation in asphalt pavements, Journal of Traffic and Transportation Engineering (English Edition), https://doi.org/10.1016/j.jtte.2018.07.003
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J. Traffic Transp. Eng. (Engl. Ed.) xxxx; xxx (xxx): xxx
Fig. 8 e Distribution fitting of S3, DGN3j (j ¼ 1, 2, …, 6). (a) Y1, distribution: Burr (4P). (b) Y2, distribution: Gen. Extreme. (c) Y3, distribution: Gen. Extreme. (d) Y4, distribution: Gen. Extreme. (e) Y5, distribution: Dagum (4P). (f) Y6, distribution: Gen. Extreme.
and comprehensive tool for the road authorities of a highway in the framework of planning and optimizing any preventative actions during a year, when passing from wet to dry periods with a view to enhancing road users' safety.
on the pairs of measured and estimated GND values. RMSPE is defined as follow.
3.3.
From Fig. 11, it can be shown that the RMSPE is 13%, which is considered as a low value that represents a good trend (Veerasamy et al., 2011) for the particular sort of data and hence, the estimated data of GND values constitutes a sufficiently satisfactory approach of the measured ones. Consequently, the reference percentage change of 35% was found to be applicable for the estimation of skid resistance seasonal variation in this additional highway interurban section.
Validation process
At this stage, the performance and accuracy of the reference percentage change of 35% is assessed through the estimation of GN values during the dry season in the additional interurban highway pavement section under investigation. Measured GNW and GND values are available from field measurements. As such, based on measured GNW values, GND values are estimated through Eq. (10). Fig. 10 illustrates the comparison between the measured and estimated GND values, along this particular highway section. First, it can be noticed that in most cases, the estimated GND values are clearly lower than the measured ones, but this is acceptable as being in favor of safety. Afterwards, with the view to evaluating the estimated DGNref (35%), root mean square percent of error (RMSPE) criterion (Eq. (11)) is adapted
vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ! u n u1 X GNestimated GNmeasured 2 t i i $100% RMSPE ¼ n i¼1 GNmeasured i
4.
(11)
Discussion
According to the present investigation results, for the case of the considered urban highway pavement sections the effect of
Please cite this article as: Plati, C et al., Quantification of skid resistance seasonal variation in asphalt pavements, Journal of Traffic and Transportation Engineering (English Edition), https://doi.org/10.1016/j.jtte.2018.07.003
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J. Traffic Transp. Eng. (Engl. Ed.) xxxx; xxx (xxx): xxx
Fig. 9 e Distribution fitting of S4, DGN4j (j ¼ 1, 2, 4, 5, 6). (a) Y1, distribution: Burr (4P). (b) Y2, distribution: Gen. Extreme. (c) Y4, distribution: Dagum (4P). (d) Y5, distribution: Gen. Extreme. (e) Y6, distribution: Gen. Extreme.
Table 2 e Results of the goodness of fit tests for S1. Year
Distribution
Test
Test statistic
Significance level (a0 )
Y1 Y2 Y3 Y4 Y5 Y6
Non available Burr (4P) Dagum (4P) Non available Non available e
e KolmogoroveSmirnov Anderson-Darling e e e
e 0.015 1.381 e e e
e 0.01 0.10 e e e
Table 3 e Results of the goodness of fit tests for S2. Year
Distribution
Test
Test statistic
Significance level (a0 )
Y1 Y2 Y3 Y4 Y5 Y6
Non available Gen. Extreme Dagum (4P) Dagum (4P) Burr (4P) Non available
e KolmogoroveSmirnov Anderson-Darling Anderson-Darling Anderson-Darling e
e 0.023 2.332 0.658 1.484 e
e 0.20 0.05 0.20 0.10 e
Please cite this article as: Plati, C et al., Quantification of skid resistance seasonal variation in asphalt pavements, Journal of Traffic and Transportation Engineering (English Edition), https://doi.org/10.1016/j.jtte.2018.07.003
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Table 4 e Results of the goodness of fit tests for S3. Year
Distribution
Test
Test statistic
Significance level (a0 )
Y1 Y2 Y3 Y4 Y5 Y6
Burr (4P) Gen. Extreme Gen. Extreme Gen. Extreme Dagum (4P) Gen. Extreme
KolmogoroveSmirnov KolmogoroveSmirnov KolmogoroveSmirnov Anderson-Darling Anderson-Darling Anderson-Darling
0.049 0.076 0.030 0.621 0.436 1.261
0.20 0.01 0.20 0.20 0.20 0.20
Table 5 e Results of the goodness of fit tests for S4. Year
Distribution
Test
Test statistic
Significance level (a0 )
Y1 Y2 Y3 Y4 Y5 Y6
Burr (4P) Gen. Extreme Non available Dagum (4P) Gen. Extreme Gen. Extreme
Anderson-Darling Anderson-Darling e Anderson-Darling KolmogoroveSmirnov KolmogoroveSmirnov
1.541 2.809 e 3.578 0.034 0.044
0.10 0.02 e 0.01 0.20 0.05
Table 6 e Representative values of DGNij (%) (j ¼ 1, 2, …, 6). i-section
S1 S2 S3 S4
j-year Y1
Y2
Y3
Y4
Y5
Y6
Non defined Non defined 39 45
19 38 34 37
38 30 45 Non defined
Non defined 17 26 23
Non defined 32 28 32
Non defined Non defined 50 42
Table 7 e Descriptive statistics of the overall percentage change DGNref (%). Measure Mean Standard deviation 25% Median 50% 75%
Descriptive statistics 33.820 9.308 40.500 34.000 27.000
Fig. 11 e RMSPE of GND values.
Fig. 10 e GND values along the additional interurban highway section.
seasonal variation in skid resistance is found to be around 35% (percentage change of the GN index) when moving from wet to dry seasons. The estimated reference change illustrates an indicative decrease (35%) in skid resistance level when
measurements are performed during dry months with respect to the ones performed during the wet months. Besides, the lower skid resistance values for the dry period have also been stated in several studies (Ahammed and Tighe, 2009; Bijsterveld and Del Val, 2015; Hill and Henry, 1981; Williamsleatag, 2015; Wilson, 2006). The change of 35% in skid resistance between wet and dry period was estimated based on data collected from an urban highway with intense traffic volume and mild climate conditions, while it was validated using data from an interurban highway with lower traffic volume but heavier precipitation with significant likelihood of occurrence during the dry months as well. It seems that the only common element between the two highways is the same asphalt mix type of the wearing course. This probably produces evidence in support of the statement that the seasonal variation of skid resistance may be dependent on the asphalt mix type.
Please cite this article as: Plati, C et al., Quantification of skid resistance seasonal variation in asphalt pavements, Journal of Traffic and Transportation Engineering (English Edition), https://doi.org/10.1016/j.jtte.2018.07.003
J. Traffic Transp. Eng. (Engl. Ed.) xxxx; xxx (xxx): xxx
However, in order to strengthen the previous remark, it would be valuable to thoroughly investigate the seasonal variation effect on skid resistance evolution in a case of highway sections with the same climatic conditions and similar traffic volume data but different types of asphalt mix in the wearing course. Hence, it would be potentially feasible to appraise and investigate a possible modification on how the suggested reference percentage change might be calibrated. This would give increased confidence to road authorities that wish to be aware of the current skid resistance levels in the framework of a general pavement monitoring system by considering as many parameters as possible, including potential materials effect.
5.
Conclusions
The present investigation suggested a process for the quantification of the seasonal variation effect that strongly affects skid resistance. The quantification process accomplished by advanced statistical analysis of skid resistance data results in a percentage change between GN values obtained shortly after wet seasons and those obtained shortly after dry seasons. Specifically, it is estimated a percentage reference change of 35% that seemed to sufficiently quantify the seasonal variation in skid resistance levels between wet and dry seasons. This percentage change is successfully validated suggesting locally a promising estimator of skid resistance for a wet or a dry period, which is dependent on the conditions of highway under investigation. Although quantitative discrepancies might be expected through different measuring devices, the proposed quantification process can be considered as reasonable and applicable, as the main emphasis of this study was to demonstrate the practical utilities of the quantification process without univocally adopting the reference change of 35% elsewhere. Upon a wider use, the estimation of the seasonal variation effect in skid resistance needs further investigation taking into account various pavement sections with different types and materials of wearing course, traffic volume and environmental conditions including the temperature effect. Until then, the suggested approach might constitute an efficient process so as to provide road authorities and highway operators with the knowledge of skid resistance seasonal evolution, which is important for scheduling preventative actions in favor of road safety, well ahead.
Conflict of interest The authors do not have any conflict of interest with other entities or researchers.
references
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Ahammed, M.A., Tighe, S.L., 2009. Early-life, long-term, and seasonal variations in skid resistance in flexible and rigid pavements (technical paper). Transportation Research Record 2094, 112e120. American Society for Testing Materials (ASTM), 2015. Standard Specification for a Size 10 4e5 Smooth-Tread Friction Test Tire. ASTM E1844-08. ASTM International, West Conshohocken. Anupam, K., Srirangam, S.K., Scarpas, A., et al., 2013. Influence of temperature on tire-pavement friction-II: analysis. Transportation Research Record 2369, 114e124. Araujo, V.M.C., Bessa, I.S., Castelo Branco, V.T.F., 2015. Measuring skid resistance of hot mix asphalt using the aggregate image measurement system (AIMS). Construction and Building Materials 98, 476e481. AUSTROADS, 2005. Guidelines for the Management of Road Surface Skid Resistance. AUSTROADS, Sydney. Bessa, I.S., Branco, V., Soares, J.B., 2014. Evaluation of polishing and degradation resistance of natural aggregates and steel slag using the aggregate image measurement system. Road Materials and Pavement Design 15 (2), 385e405. Bijsterveld, W.V., Del Val, M.A., 2015. Towards quantification of seasonal variations in skid resistance measurements. Road Materials and Pavement Design 17 (2), 477e486. Choi, Y., 2011. Review of Skid Resistance and Measurement Methods. AP-T177/11. AUSTROADS, Sydney. D'Agostino, R.B., Stephens, M.A., 1986. Goodness-of-Fit Techniques. Marcel Dekker, New York. Do, M.-T., Cerezo, V., Beautru, Y., et al., 2013. Modeling of the connection road surface microtexture/water depth/friction. Wear 302 (1e2), 1426e1435. Do, M.-T., Tang, Z., Kane, M., et al., 2007. Pavement polishing e development of a dedicated laboratory test and its correlation with road results. Wear 263 (1e6), 36e42. Donbavand, J., Cook, D., 2005. Procedures for correcting seasonal variations. In: The Conference “Surface Friction”, Christchurch, 2005. Dunford, A., Roe, P.G., 2010. BOS Slag as a Surface Course Aggregate. Report-Ppr647. Transport Research Laboratory, Berks. Findlay Irvine Ltd, 2002. Road Base Grip Tester Survey Software for Roads (User Manual). Findlay Irvine Ltd, Penicuik. Flintsch, G.W., McGhee, K.K., De Leon, E.I., et al., 2012. The Little Book of Tire Pavement Friction. Version 1.0. Pavement Surface Properties Consortium, Blackburg. Flintsch, G.W., De Leon, E., McGhee, K.K., et al., 2003. Pavement Surface Macrotexture Measurement and Application. Transportation Research Board (TRB), Washington DC. Fwa, T.F., 2017. Skid resistance determination for pavement management and wet-weather road safety. International Journal of Transportation Science and Technology 6 (3), 217e227. Georgiou, P., Loizos, A., 2014. A laboratory compaction approach to characterize asphalt pavement surface friction performance. Wear 311 (1e2), 114e122. Hall, J.W., Smith, K.L., Titus-Glover, L., et al., 2009. Guide for Pavement Friction. Project 01-43. National Cooperative Highway Research Program (NCHRP), Washington DC. Hill, B.J., Henry, J.J., 1981. Short-term, weather-related skid resistance variations. Transportation Research Record 836, 76e81. Kane, M., Do, M.-T., Cerezo, V., et al., 2017. Contribution to pavement friction modelling: an introduction of the wetting effect. International Journal of Pavement Engineering, https://doi.org/10.1080/10298436.2017.1369776. Kane, M., Zhao, D., De-Larrard, F., et al., 2012. Laboratory evaluation of aggregate polishing as a function of load and velocity: application to the prediction of damages on skid resistance of road surfaces due to trucks and passenger cars. Road Materials and Pavement Design 13 (2), 312e326.
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Khasawneh, M., Smadi, M., Zelelew, H., 2015. Investigation of the factors influencing wavelet-based macrotexture values for HMA pavements. Road Materials and Pavement Design 17 (3), 779e791. Kogbara, R.B., Masad, E.A., Kassem, E., et al., 2016. A state-of-theart review of parameters influencing measurement and modeling of skid resistance of asphalt pavements. Construction and Building Materials 114, 602e617. Kotz, S., Nadarajah, S., 2000. Extreme Value Distributions: Theory and Applications. Imperial College Press, London. Losa, M., Leandri, P., Bacci, R., 2008. Monitoring and evaluating performance requirements of flexible road pavements. In: The 1st International Symposium on Transportation and Development Innovative Best Practices, Beijing, 2008. Masad, E., Rezaei, A., Chowdhury, A., 2010. Field Evaluation of Asphalt Mixture Skid Resistance and its Relationship to Aggregate Characteristics. Report 0-5627-3. Texas A&M Transportation Institute, College Station. Meegoda, J.N., Gao, S., 2015. Evaluation of pavement skid resistance using high speed texture measurement. Journal of Traffic and Transportation Engineering (English Edition) 2 (6), 382e390. Mulry, B., Brennan, M.J., Sheahan, J.N., 2012. A model for adjusting scrim skid resistance data to reflect seasonal variation. In: 5th Eurasphalt & Eurobitumen Congress, Istanbul, 2012. Oliver, J.W.H., Tredrea, P.F., Pratt, D.N., 1988. Seasonal Variation of Skid Resistance in Australia. Special Report No. 37. Australian Road Research Board, Melbourne. Plati, C., Georgouli, K., Loizos, A., 2014. Utilizing the seasonal variations of skid resistance to plan pavement preventive maintenance. In: Losa, M., Papagiannakis, T. (Eds.), Sustainability, Eco-Efficiency, and Conservation in Transportation Infrastructure Asset Management. CRC Press, Pisa, pp. 509e518. , F.G., Vaiana, R., 2015. A study on the relationship between Pratico mean texture depth and mean profile depth of asphalt pavements. Construction and Building Materials 101 (1), 72e79. Prowell, B.D., Xie, H., Cooley, A.L., et al., 2003. Relationships between pavement friction and material properties at NCAT test track. In: Transportation Research Board 82nd Annual Meeting, Washington DC, 2003. Ragland, D.R., Oh, M., Chan, C.Y., 2010. Evaluation of Traffic and Environment Effects on Skid Resistance and Safety Performance of Rubberized Open-Grade Asphalt Concrete. Research Report: UCB-ITS-PRR-2010-14. Institute of Transportation Studies, University of California, Berkley. Rezaei, A., Masad, E., Chowdhury, A., 2011. Development of a model for asphalt pavement skid resistance based on aggregate characteristics and gradation. Journal of Transportation Engineering 137 (12), 863e873. Rodriguez, O.D.R., 2009. Evaluation of Pavement Surface Friction Seasonal Variations (Master thesis). Virginia Polytechnic Institute and State University, Blacksburg. Rogers, M.P., Gargett, T., 1991. Skidding resistance standard for the national road network. Highways and Transportation 38 (4), 10e13. Srirangam, K., Anupam, K., Kasbergenb, C., et al., 2017. Analysis of asphalt mix surface-tread rubber interaction by using finite element method. Journal of Traffic and Transportation Engineering (English Edition) 4 (4), 395e402. Srirangam, K., Anupam, K., Scarpas, A., et al., 2015. Development of a thermomechanical tyreepavement interaction model. International Journal of Pavement Engineering 16 (8), 721e729. Transportation Safety Board of Canada, 2010. Runway Overrun. Aviation Investigation Report A10H0004. Macdonald-Cartier International Airport, Ottawa. Vaiana, R., Capiluppi, G.F., Gallelli, V., et al., 2012. Pavement surface performances evolution: an experimental application. Procedia - Social and Behavioral Sciences 53 (3), 1149e1160.
Veerasamy, R., Rajak, H., Jain, A., et al., 2011. Validation of QSAR models - strategies and importance. International Journal of Drug Design and Discovery 2 (3), 511e519. Villani, M.M., Artamendi, I., Kane, M., et al., 2011. Contribution of hysteresis component of tire rubber friction on stone surfaces. Transportation Research Record 2227, 153e162. Wang, D., Chen, X., Yin, C., et al., 2013. Influence of different polishing conditions on the skid resistance development of asphalt surface. Wear 308, 71e78. Williamsleatag, 2015. Design Manual for Roads and Bridges. Available at: http://www.standardsforhighways.co.uk/ha/ standards/dmrb/vol7/section3/hd2818.pdf (Accessed 7 July 2015). Wilson, D.J., 2006. An Analysis of the Seasonal and Short-Term Variation of Road Pavement Skid Resistance (PhD thesis). University of Auckland, Auckland. Wilson, D.J., 2013. The Effect of Rainfall and Contaminants on Road Pavement Skid Resistance. Research Report e 515. Department of Civil and Environmental Engineering, University of Auckland, Auckland. Xie, X., Lu, G., Liu, P., et al., 2018. Influence of temperature on polishing behaviour of asphalt road surfaces. Wear 402e403, 49e56.
Christina Plati received the civil engineer degree as well the PhD degree in pavement engineering from National Technical University of Athens (NTUA). She is an assistant professor at the School of Civil Engineering of NTUA and staff member of the Laboratory of Pavement Engineering of NTUA. She has been involved in several areas of pavement engineering sector for more than 20 years and her main research interest lies in non destructive testing (NDT) techniques and analysis tools for pavement evaluation. She has been a member of a numerous of scientific committees and working groups and involved in several European activities.
Maria Pomoni received the civil engineer degree from National Technical University of Athens (NTUA). She is a junior researcher at the Laboratory of Pavement of NTUA, while she is pursuing a PhD degree on the surface frictional properties of asphalt pavements (skid resistance, macrotexture and microtexture components). She is the main contributor to a numerous of publications in international journals and conferences, while her research interests lie also on pavements sustainability aspects.
Konstantina Georgouli received the civil engineer degree as well as the PhD degree in pavement engineering from National Technical University of Athens (NTUA). She is a pavement engineer, senior researcher at the Laboratory of Pavement Engineering of NTUA. Her research interest include pavement analysis and design, pavement monitoring, evaluation and quality control. She has in excess of 10 years of experience in data collection and analysis for the assessment of pavement structural and functional condition.
Please cite this article as: Plati, C et al., Quantification of skid resistance seasonal variation in asphalt pavements, Journal of Traffic and Transportation Engineering (English Edition), https://doi.org/10.1016/j.jtte.2018.07.003
Available online at www.sciencedirect.com
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Original Research Paper
Quantification of skid resistance seasonal variation in asphalt pavements Christina Plati*, Maria Pomoni, Konstantina Georgouli Laboratory of Pavement Engineering, School of Civil Engineering, National Technical University of Athens (NTUA), Athens 15773, Greece
highlights Quantification of skid resistance seasonal variation alerts for proactive actions. Distribution fitting enables the estimation of a seasonal variation change factor. Burr e Dagum e generalized extreme value accommodate skid resistance data. Change factor is developed for an urban highway and validated for an interurban one. Seasonal variation change factor may be dependent on the asphalt mix type.
article info
abstract
Article history:
The present study deals with the quantification of skid resistance seasonal variation in
Received 15 April 2018
asphalt pavements. Consequently, skid resistance data were collected twice per year (after
Received in revised form
“wet” and “dry” seasons) along four trial sections of an urban highway with a GripTester
6 July 2018
system (during a six-year field experiment). For the estimation of a percentage reference
Accepted 16 July 2018
change (%) between wet and dry skid resistance data, a methodology is developed based on
Available online xxx
distribution fitting of the percentage change of skid resistance index due to seasonal variation impact. The outcome of the analysis is applied and successfully validated
Keywords:
through a set of skid resistance data collected from an interurban highway pavement
Asphalt pavement
section with different traffic and environmental conditions. As such the developed
Skid resistance
methodology can be considered as a promising tool for accommodating the seasonal
Seasonal variation
variation of skid resistance. Further, the resulting percentage reference change (%) enables
Quantification
the estimation of skid resistance level for the dry period based on the wet period mea-
Road safety
surements. Overall, it seems that the proposed quantification process may constitute a practical yet efficient tool for road authorities and highway operators on how to estimate the effect of seasonal variation on skid resistance levels and schedule well-ahead necessary actions for enhancing driving safety. © 2019 Periodical Offices of Chang'an University. Publishing services by Elsevier B.V. on behalf of Owner. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/).
* Corresponding author. Tel.: þ30 210 772 1363; fax: þ30 210 772 4254. E-mail addresses: [email protected] (C. Plati), [email protected] (M. Pomoni), [email protected] (K. Georgouli). Peer review under responsibility of Periodical Offices of Chang'an University. https://doi.org/10.1016/j.jtte.2018.07.003 2095-7564/© 2019 Periodical Offices of Chang'an University. Publishing services by Elsevier B.V. on behalf of Owner. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Please cite this article as: Plati, C et al., Quantification of skid resistance seasonal variation in asphalt pavements, Journal of Traffic and Transportation Engineering (English Edition), https://doi.org/10.1016/j.jtte.2018.07.003
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J. Traffic Transp. Eng. (Engl. Ed.) xxxx; xxx (xxx): xxx
1.
Introduction
1.1.
Background
The main function of a highway pavement is to serve the transportation of human beings and freights in a safe, comfortable and economic way by simultaneously protecting natural soil. This implies that a pavement structure needs to present increased serviceability condition. For that, pavements should be firstly structurally sufficient to effectively bear the increasingly demanding traffic loads under several environmental and climatic conditions. Thus, it is indispensable for road authorities to be aware of the importance of monitoring and evaluating asphalt pavements performance, with the view to improving pavements durability and road safety in conjunction with environmental and sustainable perspectives (Losa et al., 2008). Especially, pavements functional performance ensures the provision of a safe and comfortable ride to road users for a specific range of speeds. One of the most important factors that determine pavement functional performance in terms of safety is the level of friction of the wearing course (Meegoda and Gao, 2015). Safe driving depends on adequate surface friction for vehicle maneuvering, turning and braking. However, in case of a slippery road, a necessary emergency maneuvering due to skidding could cause loss of control or even a collision (Flintsch et al., 2012). It has been estimated that approximately 35% of crashes are caused at least partially because of skidding, while an improvement of 10% in the average level of skid resistance reduces skidding accident rate by 13% (Rodriguez, 2009). Skid resistance is defined as the resistive force developed when a tire is prevented from rotating along the pavement surface (AASHTO, 2008; Kogbara et al., 2016; Plati et al., 2014). Pavement surface skid resistance mainly refers to the tirepavement interaction and the resulting of that interaction, friction force (Kogbara et al., 2016). Pavement friction mainly consists of two basic components: adhesion and hysteresis (AUSTROADS, 2005; Kane et al., 2017). Adhesion results from the formation of molecular bonds between the tire tread and the pavement surface (Transportation Safety Board of Canada, 2010; Villani et al., 2011). It represents the shear force that is created when the tire tread changes its shape in order to conform to the contacting asperities of the surface aggregates (Wilson, 2006). As such, microtexture of the aggregates influences the adhesion component (Prowell et al., 2003). Hysteresis is developed due to the continuous compression and decompression phases that the tire-tread experiences when it comes in contact with the aggregates. These continuous phases cause loss of energy in form of heat (Hall et al., 2009; Wilson, 2006). Consequently, hysteresis is affected by the overall form of the aggregates, indicating that macrotexture influences this component. Practically, the aforementioned components could be more determinant in case a vehicle starts braking and slides e skids on a pavement surface, thus the aggregates of the pavement surface are in contact with the tire tread having a crucial role in immobilizing the vehicle. For the case of a dry and smooth surface, adhesion is predominant as the contacting asperities
contribute to the total friction force that helps vehicle to immobilize (Flintsch et al., 2003; Wilson, 2006). On the other hand, if the surface is wet, macrotexture is necessary to accelerate the drainage of water from the tire-pavement interface so as to prevent vehicles tires from aquaplaning (Wilson, 2013). However, the faster the vehicle moves away, the less the asperities penetrate the tire tread, consequently the adhesion component is not available as well as hysteresis loss cannot contribute to friction (Rogers and Gargett, 1991). Great efforts have been made worldwide to investigate the above friction components through advanced numerical modeling of tire-pavement interaction. Finite element method (FEM) has been widely used towards this goal (Srirangam et al., 2015, 2017). Anupam et al. (2013) have made a parametric study to incorporate the effect of several factors affecting hysteric friction of a tire slipping over an asphalt pavement surface, including pavement temperature, ambient temperature, different macrotextures profiles or tire slip ratios. Do et al. (2013) emphasized the strong correlation between friction and the number of contacting road asperities pinpointing the water-depth dependency of friction. They observed that when the water depth nearly covers the contacting asperities, their number and their height are rather essential. Profoundly, asphalt mixes characteristics of pavement wearing courses significantly affect pavements skid resistance performance and variation. Several material engineering aspects have been widely investigated in numerous relevant research studies such as the physical and shape characteristics of aggregates (Araujo et al., 2015; Bessa et al., and Vaiana, 2015; Rezaei et al., 2014; Masad et al., 2010; Pratico 2011; Vaiana et al., 2012). Also among them, the aggregate polishing agents, the affinity between aggregate and bitumen and the wetness conditions constitute valuable quantitative and qualitative information for field or laboratory skid resistance assessment or even for the development of surface friction predictive models (Wang et al., 2013). To that extent, the input of traffic data is also important. Particularly, the type of vehicles that move on road surfaces is believed to greatly influence the skid resistance level (Khasawneh et al., 2015). Recent studies have shown that the skid resistance degree is more closely affected by the number of duty vehicles per lane per day, rather than the total number of all vehicles (Mulry et al., 2012; Ragland et al., 2010). Also, the utilized measuring device may affect the skid resistance degree due to the differences in measuring principles, this highlights the importance for the harmonization of pavement friction measuring systems (Kogbara et al., 2016). From a more practical engineering perspective, numerous studies (Ahammed and Tighe, 2009; Bijsterveld and Del Val, 2015; Fwa, 2017; Hill and Henry, 1981; Oliver et al., 1988; Prowell et al., 2003) have focused on skid resistance seasonal variation in the short-term (i.e., throughout a year), its contributing factors and challenges related to its quantification. Initially, once the pavement is put in service, an increase in skid resistance is expected as the film of asphalt bitumen of the surface aggregates is worn away by the traffic flow and hence, macrotexture and microtexture components govern. Then, once the aggregates are exposed without been covered
Please cite this article as: Plati, C et al., Quantification of skid resistance seasonal variation in asphalt pavements, Journal of Traffic and Transportation Engineering (English Edition), https://doi.org/10.1016/j.jtte.2018.07.003
J. Traffic Transp. Eng. (Engl. Ed.) xxxx; xxx (xxx): xxx
by asphalt bitumen, given a constant traffic flow, any fluctuations in skid resistance mainly occur because of the seasonal and/or short-term environmental variations (Wilson, 2006). Also, it seems that the amount of the asperities of aggregates used in the asphalt-mixture (microtexture) can also influence the polishing behavior of an asphalt road surface and consequently affect pavement functional performance in terms of skid resistance (Georgiou and Loizos, 2014; Kane et al., 2012). Actually, the major process that prevails and affects seasonal variation has not yet been clearly understood (Ahammed and Tighe, 2009; Hall et al., 2009). Generally, a typical pavement surface presents different performance in terms of skid resistance levels during wet and dry seasons. The main hypothesis is that during dry summer months, skid resistance is lower as vehicle tires cause disintegrated debris to continuously ground up, producing fine dust which subsequently extends the polishing of the surface aggregates. On the other hand, during the wet winter months loose debris is easily cleansed, under the influence of rainfall and traffic, acting against the fine dust formation. In that case, coarse debris is more predominant and under the movement of vehicle tires, pavement surface becomes rougher resulting in higher skid resistance levels (Dunford and Roe, 2010; Do et al., 2007; Donbavand and Cook, 2005). Temperature is listed as a factor affecting the available pavement skid resistance (Xie et al., 2018). However, modern rubber compounds are formulated to remove the effect of temperature in friction under normal running speeds and temperatures. Typically, friction is reduced at high temperatures where the rubber becomes soft because hysteresis is affected by the viscoelastic property of the tire. Also the lower the temperature, the faster the friction decreases (Xie et al., 2018). These higher temperatures are typically reached when a lot of breaking is performed (Anupam et al., 2013). In view of the above, it becomes obvious that accurate quantification of skid resistance seasonal variation is a rather complex task. As such, it has not been yet possible to jointly consider the potential impact of different affecting factors on skid resistance seasonal evolution, as it is difficult to fully incorporate the influence caused to skid resistance seasonal variation by each one of them (Bijsterveld and Del Val, 2015) and univocally apply it in various road conditions. However, considering that highway agencies propose recommendations for safety against skidding based either on past experience or on skid resistance measurements (Anupam et al., 2013), the assessment and interpretation of any field measurements needs probably to be strictly aligned with the season of year they are undertaken due to the seasonal variations effects (Ragland et al., 2010). Towards this direction, specific standards exist (Williamsleatag, 2015), adopting the terminology wet and dry skid resistance measurements and recommend skid resistance levels to be measured under standardized conditions. This should be accomplished during the periods where precipitation occurrence is less frequent (i.e., dry seasons) and the lowest measured skid resistance values are observed. However, as a common practice skid resistance measurements are undertaken more than once per year (after wet and dry
3
seasons) in order to capture the seasonal variation effect on skid resistance evolution. As such, it still remains a big challenge for road authorities and pavement engineers to develop methodological approaches or even establish a process for the quantification of skid resistance seasonal variation in asphalt pavements so as to organize, if needed, any preventive actions well-ahead.
1.2.
Objective
The motivation of the current research study lies upon the fact that by performing skid resistance measurements during the wet season (after the period of intense precipitation) on the one hand, and having knowledge of the evolution of skid resistance due to seasonal variations on the other hand, a rough, yet reliable estimation of the expected skid resistance level during the dry seasons will be feasible. In case of potentially low expected levels during the dry season, appropriate intervention actions will have to be scheduled wellahead, in favor of road safety. Thus, quantifying skid resistance seasonal variation in asphalt pavements may constitute a useful and practical approach towards enhancing the knowledge of its evolution within a year. A previous preliminary study conducted by Plati et al. (2014) focused on the aforementioned quantification through a simplified approach. On these grounds, the current research aims to investigate a relative change factor for quantifying skid resistance seasonal variation that occurs within a year, concentrating on the rainfall effect, so as to assist road authorities and highway operators on decision making of maintenance activities with a view to enhancing driving safety. Towards this, skid resistance data were collected twice per year (after wet and dry seasons) from four trial sections of an urban highway with a GripTester system during a six-year field experiment (Findlay Irvine Ltd, 2002). For the estimation of a percentage reference change (%) between wet and dry skid resistance data, a methodology is developed based on distribution fitting of the percentage change of skid resistance index due to seasonal variation impact. The outcome of the analysis is validated through a set of skid resistance data collected from an interurban highway, as described in the following.
2.
Methodology
2.1.
Test sites and field experiment
It has been already mentioned that skid resistance data were collected from four trial sections (S1eS4) of an urban highway over a six-year period (Y1eY6). Experimental measurements were undertaken along the outer wheel path of the right lane, utilizing a GripTester system (Choi, 2011; Findlay Irvine Ltd, 2002). The GripTester is a fixed-slip device that can be used for effectively monitoring the surface of highways' pavements. The device consists of a three-wheeled system and a smoothtread tire is fitted to the central wheel, utilized for skid resistance measurements, according to ASTM E1844 standard (ASTM, 2015). The axle of the test wheel is connected to a chain-system that controls the test wheel's slip speed by
Please cite this article as: Plati, C et al., Quantification of skid resistance seasonal variation in asphalt pavements, Journal of Traffic and Transportation Engineering (English Edition), https://doi.org/10.1016/j.jtte.2018.07.003
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providing a constant slip ratio of 14%. Also, a water-pump of the GripTester ensures a constant 0.5 mm water-film supply on the pavement surface, allowing skid resistance measurements at a certainly wet pavement surface. The system reports an average index of skid resistance measurements, called the grip number (GN), which is averaged at 10 m intervals for this study. In general, GN index is dimensionless and ranges from 0 to 1. Increased skid resistance is expected for higher GN values. Regarding the asphalt mixtures characteristics of the pavement sections being investigated, they are of hot mix asphalt concrete, surfaced with an antiskid surface layer, with an open mixture type corresponding to the O-5 mix designation, as defined in ASTM standards (D-3515). Steel slag was utilized in the mix design as an aggregate and the mix produced utilizing a 25-55/70 asphalt grade penetration modified with polymer modified bitumen with a binder content of 4.0% by mass of the mixture, while the air void content of the asphalt mix was 11.5% by mass of the mixture (Plati et al., 2014). It is worthwhile mentioning that the sections under investigation were constructed at different time periods with the latest to have been constructed three years after the older one. The length of the sections is between 10 and 50 km. Based on the extensive routine pavement monitoring all the pavements were structurally sound without any indication of rutting, roughness problems, or hydroplaning. Skid resistance measurements were conducted shortly after a 5-month period of precipitation (wet season) and after a 5-month period of low precipitation occurrence and higher
temperatures (dry season). Also air temperature was recorded, ranging from 2 C to 15 C for wet seasons and from 25 C to 35 C for dry seasons. As it has been already mentioned, for validation purposes, skid resistance measurements were also performed along another pavement section of an interurban highway over a period of one year. The pavement age of the interurban highway pavement section was 3 years, while its length is about 20 km. Measuring principles as well as the type of asphalt wearing course were the same. However, climatic conditions and traffic volume differed significantly. Air temperature during the measurements ranged from 4 C to 12 C in the wet season, while in the dry season, temperature ranged from 20 C to 25 C. It is also noted that the climate in the area of the interurban highway pavement section is rather humid with considerable rainfall occurrence during the year. Additionally, traffic volume is less intense than in the urban highway considering relevant information of the vehicle flow of both duty vehicles and passengers cars.
2.2.
Collected data
According to collected skid resistance data from the urban pavement sections, noticeable differences exist between GN values measured in wet seasons (GNW) and those measured in dry seasons (GND) during each year. Box plots in Figs. 1e4 illustrate the range of the collected data at the four trial sections (S1eS4) during the six-year period (Y1eY6). The central line in the box indicates the average value of the data sample, given that the coefficients of variation for each case are at a low level (less than 20%). The upper and the lower hinges of
Fig. 1 e Measured GN values from S1. (a) GNW. (b) GND.
Fig. 2 e Measured GN values from S2. (a) GNW. (b) GND. Please cite this article as: Plati, C et al., Quantification of skid resistance seasonal variation in asphalt pavements, Journal of Traffic and Transportation Engineering (English Edition), https://doi.org/10.1016/j.jtte.2018.07.003
J. Traffic Transp. Eng. (Engl. Ed.) xxxx; xxx (xxx): xxx
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Fig. 3 e Measured GN values from S3. (a) GNW. (b) GND.
Fig. 4 e Measured GN values from S4. (a) GNW. (b) GND.
the box indicate the 75% and the 25% percentile respectively of the data sample. Finally, the whiskers (the ends of the vertical lines) indicate the minimum and maximum values of each data sample. As expected, the average GNW values are clearly higher than the corresponding GND ones for all sections. GNW generally range from 0.41 to 0.65. In these urban highway sections, GNW values are generally satisfactory owing to winter rainfalls. However, the combined effect of the traffic volume in conjunction to the presence of contaminants on the pavement surface that cannot be totally moved away by the wet season rainfalls, leads to poor skidding pavement performance during the dry season, reflected through low GND values. Afterwards, it can be observed that the data sets of S1 present greater variance than the other sections for each year. This might happen due to remarkable deviations in the traffic flow level within S1. As such, data sets will be further analyzed in the following chapter so as to investigate their appropriateness for being included in the development of the quantification process. As far as the interurban highway section is concerned, Fig. 5 illustrates the average GN values from the data collected during the year of measurements (3rd year of in-service period) for both wet and dry season (GNW and GND). It is worthwhile mentioning that for this interurban highway section the average GNW value is 0.74 while the GND one is 0.53. According to this, it should be stated that the degradation in the interurban highway skid resistance level during dry season remains in a satisfactory level of GND (that of 0.53 GN). This is more likely to happen because of climatic
conditions in the geographic area of the interurban highway that can be rainy during the dry (summer) period as well, as opposed to the area of the urban highway.
2.3.
Data analysis
Upon quantifying the seasonal variation effect in GN values recorded from the urban highway pavement sections, a percentage change between GNW and GND values eDGNij (%) for the i-section and j-year is determined according to Eq. (1). DGNij ¼
GND; ij GNW;ij $100% GNW;ij
(1)
where 1 i 4, 1 j 6. The adjustment of appropriate distributions at each set of DGNij data is investigated. The suitability and adaptability of
Fig. 5 e Measured GN values from the interurban highway pavement section (wet and dry).
Please cite this article as: Plati, C et al., Quantification of skid resistance seasonal variation in asphalt pavements, Journal of Traffic and Transportation Engineering (English Edition), https://doi.org/10.1016/j.jtte.2018.07.003
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J. Traffic Transp. Eng. (Engl. Ed.) xxxx; xxx (xxx): xxx
each distribution is controlled through the following tests for goodness of fit: the KolmogoroveSmirnov test and the Anderson-Darling test. The KolmogoroveSmirnov test for the estimation of goodness of fit is based on comparing an empirical distribution function with the distribution of the hypothesized function. It can be applied only to continuous distributions with estimated parameters, acting conservatively. The Anderson-Darling test is a general test to compare the fit of an observed cumulative distribution function with an expected cumulative distribution function. This test gives more weight to the tails of a data sample than the KolmogoroveSmirnov test (D'Agostino and Stephens, 1986). The KolmogoroveSmirnov test is defined as Dn ¼ supjFnðxÞ EðxÞj
(2)
where Fn(x) is the actual distribution, E(x) is the hypothesized distribution. Also, the Anderson-Darling test is defined as A2 ¼ n
n 1X fð2i 1Þ½ln FðXi Þ þ lnð1 FðXniþ1 ÞÞg n i¼1
ak1 ak xg b f ðxÞ ¼ h a ikþ1 b 1 þ xg b
a1 ak xg b f ðxÞ ¼ h a ikþ1 xg b 1þ b
(4)
and the cumulative distribution function is defined as (5)
where k is a continuous shape parameter (k > 0), a is a continuous shape parameter (a > 0), b is a continuous scale
(6)
and the cumulative distribution function is defined as " FðxÞ ¼ 1 þ
a #k xg b
(7)
The probability density function of Gen. Extreme is defined as 8 i h > 1 11 1 > > < ð1 þ kzÞ k exp ð1 þ kzÞ k s f ðxÞ ¼ > 1 > > : exp½ z expðzÞ s
(3)
where F(Xi) is the actual distribution. Based on the continuous nature of pavement skid resistance data that constitutes identically distributed random values describing a physical event which occurs with small probability, three continuous distributions have been preliminarily found to be more suitable for this sort of data amongst numerous continuous distributions. These distributions include the Burr distribution described through four parameters e Burr (4P), the Dagum distribution described as well through four parameters e Dagum (4P) and finally the generalized extreme value distribution e Gen. Extreme (Kotz and Nadarajah, 2000). To best of the authors' knowledge, the present study constitutes a first investigation approach where these distributions are utilized to accommodate skid resistance data of asphalt pavements. Below, the mathematical background of these distributions is briefly presented. The probability density function of Burr (4P) is defined as
a k xg FðxÞ ¼ 1 1 þ b
parameter (b > 0), g is a continuous location parameter (g x < þ∞). The probability density function of Dagum (4P) is defined as
ks0 (8) k¼0
and the cumulative distribution function is defined as
FðxÞ ¼
8 i h > < exp ð1 þ kzÞ1k
ks0
> : exp½expðzÞ
k¼0
(9)
where z ≡ (xm)/s, s is a continuous scale parameter, m is a continuous location parameter.
3.
Results
3.1.
Distribution fitting
The considered distributions for each set of data corresponding to a specific section (S1eS4) of the investigated road network and analysis year (Y1eY6) are shown in Table 1. The term “non available” stands for the cases where none of the available distributions can be fitted to the respective data. The histograms as well as the distribution curves describing the input data are presented in Figs. 6 e 9. Tables 2e5 show the results of the KolmogoroveSmirnov test (Eq. (2)) and the Anderson-Darling test (Eq. (3)) for goodness of fit for each data sample. The most appropriate test for each case is considered with the view to better adapting the selected distribution. However, as it can be observed there are DGNij samples for which none distribution, from the utilized ones, could be fitted. One possible reason behind that could be the greater variations in those DGNij samples that made difficult the distribution fitting. In a way, this indicates that those samples may not
Table 1 e Distributions for DGNij (%) samples per year (j ¼ 1, 2, …, 6) and section (i ¼ 1, 2, 3, 4). i-section
S1 S2 S3 S4
j-year Y1
Y2
Y3
Y4
Y5
Y6
Non available Non available Burr (4P) Burr (4P)
Burr (4P) Gen. Extreme Gen. Extreme Gen. Extreme
Dagum (4P) Dagum (4P) Gen. Extreme Non available
Non available Dagum (4P) Gen. Extreme Dagum (4P)
Non available Burr (4P) Dagum (4P) Gen. Extreme
Non available Non available Gen. Extreme Gen. Extreme
Please cite this article as: Plati, C et al., Quantification of skid resistance seasonal variation in asphalt pavements, Journal of Traffic and Transportation Engineering (English Edition), https://doi.org/10.1016/j.jtte.2018.07.003
J. Traffic Transp. Eng. (Engl. Ed.) xxxx; xxx (xxx): xxx
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Fig. 6 e Distribution fitting of S1, DGN1j (j ¼ 2, 3). (a) Y2, distribution: Burr (4P). (b) Y3, distribution: Dagum (4P).
be considered as appropriate for further analysis, and as such the exclusion of them might be an acceptable approach. The significance level (a0 ) indicates the maximum fixed level that is considered for common statistic reasons, up to which the null hypothesis (Ho) can be accepted. Also for this level, a critical value was obtained from a standard table and if it was greater than the corresponding test statistic value, the assumed distribution was accepted for the data description.
3.2.
Development of a reference change factor
Based on the above, Table 6 summarizes the estimated representative DGNij, stemming from the distribution fitting at each section for every year of the investigation. Actually, those DGNij values present the maximum value of the probability density function, in other words those values are most likely to be observed (sampled) in each data set and hence, they are considered as representative.
In an attempt to come up with an overall relative reference percentage change (DGNref (%)) irrespective of the section under investigation or the analysis year, all DGNij values from Table 6 are considered to form a new data sample which is subjected to an additional statistical analysis. Herein descriptive statistical indicators, shown in Table 7, are used owing to the fact that the new data sample consists of few values. Towards establishing a safe approach and taking into consideration the above, a relative reference percentage change DGNref of 35% between wet and dry seasons for the particular urban highway sections is selected. Accordingly, an estimation of GND values can be suggested as following based on wet season e skid resistance measurements. GND ¼ 65%GNW
(10)
It might be argued that the information provided by the previous sample, albeit conservative, could constitute a useful
Fig. 7 e Distribution fitting of S2, DGN2j (j ¼ 2, 3, 4, 5). (a) Y2, distribution: Gen. Extreme. (b) Y3, distribution: Dagum (4P). (c) Y4, distribution: Dagum (4P). (d) Y5, distribution: Burr (4P). Please cite this article as: Plati, C et al., Quantification of skid resistance seasonal variation in asphalt pavements, Journal of Traffic and Transportation Engineering (English Edition), https://doi.org/10.1016/j.jtte.2018.07.003
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J. Traffic Transp. Eng. (Engl. Ed.) xxxx; xxx (xxx): xxx
Fig. 8 e Distribution fitting of S3, DGN3j (j ¼ 1, 2, …, 6). (a) Y1, distribution: Burr (4P). (b) Y2, distribution: Gen. Extreme. (c) Y3, distribution: Gen. Extreme. (d) Y4, distribution: Gen. Extreme. (e) Y5, distribution: Dagum (4P). (f) Y6, distribution: Gen. Extreme.
and comprehensive tool for the road authorities of a highway in the framework of planning and optimizing any preventative actions during a year, when passing from wet to dry periods with a view to enhancing road users' safety.
on the pairs of measured and estimated GND values. RMSPE is defined as follow.
3.3.
From Fig. 11, it can be shown that the RMSPE is 13%, which is considered as a low value that represents a good trend (Veerasamy et al., 2011) for the particular sort of data and hence, the estimated data of GND values constitutes a sufficiently satisfactory approach of the measured ones. Consequently, the reference percentage change of 35% was found to be applicable for the estimation of skid resistance seasonal variation in this additional highway interurban section.
Validation process
At this stage, the performance and accuracy of the reference percentage change of 35% is assessed through the estimation of GN values during the dry season in the additional interurban highway pavement section under investigation. Measured GNW and GND values are available from field measurements. As such, based on measured GNW values, GND values are estimated through Eq. (10). Fig. 10 illustrates the comparison between the measured and estimated GND values, along this particular highway section. First, it can be noticed that in most cases, the estimated GND values are clearly lower than the measured ones, but this is acceptable as being in favor of safety. Afterwards, with the view to evaluating the estimated DGNref (35%), root mean square percent of error (RMSPE) criterion (Eq. (11)) is adapted
vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ! u n u1 X GNestimated GNmeasured 2 t i i $100% RMSPE ¼ n i¼1 GNmeasured i
4.
(11)
Discussion
According to the present investigation results, for the case of the considered urban highway pavement sections the effect of
Please cite this article as: Plati, C et al., Quantification of skid resistance seasonal variation in asphalt pavements, Journal of Traffic and Transportation Engineering (English Edition), https://doi.org/10.1016/j.jtte.2018.07.003
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J. Traffic Transp. Eng. (Engl. Ed.) xxxx; xxx (xxx): xxx
Fig. 9 e Distribution fitting of S4, DGN4j (j ¼ 1, 2, 4, 5, 6). (a) Y1, distribution: Burr (4P). (b) Y2, distribution: Gen. Extreme. (c) Y4, distribution: Dagum (4P). (d) Y5, distribution: Gen. Extreme. (e) Y6, distribution: Gen. Extreme.
Table 2 e Results of the goodness of fit tests for S1. Year
Distribution
Test
Test statistic
Significance level (a0 )
Y1 Y2 Y3 Y4 Y5 Y6
Non available Burr (4P) Dagum (4P) Non available Non available e
e KolmogoroveSmirnov Anderson-Darling e e e
e 0.015 1.381 e e e
e 0.01 0.10 e e e
Table 3 e Results of the goodness of fit tests for S2. Year
Distribution
Test
Test statistic
Significance level (a0 )
Y1 Y2 Y3 Y4 Y5 Y6
Non available Gen. Extreme Dagum (4P) Dagum (4P) Burr (4P) Non available
e KolmogoroveSmirnov Anderson-Darling Anderson-Darling Anderson-Darling e
e 0.023 2.332 0.658 1.484 e
e 0.20 0.05 0.20 0.10 e
Please cite this article as: Plati, C et al., Quantification of skid resistance seasonal variation in asphalt pavements, Journal of Traffic and Transportation Engineering (English Edition), https://doi.org/10.1016/j.jtte.2018.07.003
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J. Traffic Transp. Eng. (Engl. Ed.) xxxx; xxx (xxx): xxx
Table 4 e Results of the goodness of fit tests for S3. Year
Distribution
Test
Test statistic
Significance level (a0 )
Y1 Y2 Y3 Y4 Y5 Y6
Burr (4P) Gen. Extreme Gen. Extreme Gen. Extreme Dagum (4P) Gen. Extreme
KolmogoroveSmirnov KolmogoroveSmirnov KolmogoroveSmirnov Anderson-Darling Anderson-Darling Anderson-Darling
0.049 0.076 0.030 0.621 0.436 1.261
0.20 0.01 0.20 0.20 0.20 0.20
Table 5 e Results of the goodness of fit tests for S4. Year
Distribution
Test
Test statistic
Significance level (a0 )
Y1 Y2 Y3 Y4 Y5 Y6
Burr (4P) Gen. Extreme Non available Dagum (4P) Gen. Extreme Gen. Extreme
Anderson-Darling Anderson-Darling e Anderson-Darling KolmogoroveSmirnov KolmogoroveSmirnov
1.541 2.809 e 3.578 0.034 0.044
0.10 0.02 e 0.01 0.20 0.05
Table 6 e Representative values of DGNij (%) (j ¼ 1, 2, …, 6). i-section
S1 S2 S3 S4
j-year Y1
Y2
Y3
Y4
Y5
Y6
Non defined Non defined 39 45
19 38 34 37
38 30 45 Non defined
Non defined 17 26 23
Non defined 32 28 32
Non defined Non defined 50 42
Table 7 e Descriptive statistics of the overall percentage change DGNref (%). Measure Mean Standard deviation 25% Median 50% 75%
Descriptive statistics 33.820 9.308 40.500 34.000 27.000
Fig. 11 e RMSPE of GND values.
Fig. 10 e GND values along the additional interurban highway section.
seasonal variation in skid resistance is found to be around 35% (percentage change of the GN index) when moving from wet to dry seasons. The estimated reference change illustrates an indicative decrease (35%) in skid resistance level when
measurements are performed during dry months with respect to the ones performed during the wet months. Besides, the lower skid resistance values for the dry period have also been stated in several studies (Ahammed and Tighe, 2009; Bijsterveld and Del Val, 2015; Hill and Henry, 1981; Williamsleatag, 2015; Wilson, 2006). The change of 35% in skid resistance between wet and dry period was estimated based on data collected from an urban highway with intense traffic volume and mild climate conditions, while it was validated using data from an interurban highway with lower traffic volume but heavier precipitation with significant likelihood of occurrence during the dry months as well. It seems that the only common element between the two highways is the same asphalt mix type of the wearing course. This probably produces evidence in support of the statement that the seasonal variation of skid resistance may be dependent on the asphalt mix type.
Please cite this article as: Plati, C et al., Quantification of skid resistance seasonal variation in asphalt pavements, Journal of Traffic and Transportation Engineering (English Edition), https://doi.org/10.1016/j.jtte.2018.07.003
J. Traffic Transp. Eng. (Engl. Ed.) xxxx; xxx (xxx): xxx
However, in order to strengthen the previous remark, it would be valuable to thoroughly investigate the seasonal variation effect on skid resistance evolution in a case of highway sections with the same climatic conditions and similar traffic volume data but different types of asphalt mix in the wearing course. Hence, it would be potentially feasible to appraise and investigate a possible modification on how the suggested reference percentage change might be calibrated. This would give increased confidence to road authorities that wish to be aware of the current skid resistance levels in the framework of a general pavement monitoring system by considering as many parameters as possible, including potential materials effect.
5.
Conclusions
The present investigation suggested a process for the quantification of the seasonal variation effect that strongly affects skid resistance. The quantification process accomplished by advanced statistical analysis of skid resistance data results in a percentage change between GN values obtained shortly after wet seasons and those obtained shortly after dry seasons. Specifically, it is estimated a percentage reference change of 35% that seemed to sufficiently quantify the seasonal variation in skid resistance levels between wet and dry seasons. This percentage change is successfully validated suggesting locally a promising estimator of skid resistance for a wet or a dry period, which is dependent on the conditions of highway under investigation. Although quantitative discrepancies might be expected through different measuring devices, the proposed quantification process can be considered as reasonable and applicable, as the main emphasis of this study was to demonstrate the practical utilities of the quantification process without univocally adopting the reference change of 35% elsewhere. Upon a wider use, the estimation of the seasonal variation effect in skid resistance needs further investigation taking into account various pavement sections with different types and materials of wearing course, traffic volume and environmental conditions including the temperature effect. Until then, the suggested approach might constitute an efficient process so as to provide road authorities and highway operators with the knowledge of skid resistance seasonal evolution, which is important for scheduling preventative actions in favor of road safety, well ahead.
Conflict of interest The authors do not have any conflict of interest with other entities or researchers.
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Christina Plati received the civil engineer degree as well the PhD degree in pavement engineering from National Technical University of Athens (NTUA). She is an assistant professor at the School of Civil Engineering of NTUA and staff member of the Laboratory of Pavement Engineering of NTUA. She has been involved in several areas of pavement engineering sector for more than 20 years and her main research interest lies in non destructive testing (NDT) techniques and analysis tools for pavement evaluation. She has been a member of a numerous of scientific committees and working groups and involved in several European activities.
Maria Pomoni received the civil engineer degree from National Technical University of Athens (NTUA). She is a junior researcher at the Laboratory of Pavement of NTUA, while she is pursuing a PhD degree on the surface frictional properties of asphalt pavements (skid resistance, macrotexture and microtexture components). She is the main contributor to a numerous of publications in international journals and conferences, while her research interests lie also on pavements sustainability aspects.
Konstantina Georgouli received the civil engineer degree as well as the PhD degree in pavement engineering from National Technical University of Athens (NTUA). She is a pavement engineer, senior researcher at the Laboratory of Pavement Engineering of NTUA. Her research interest include pavement analysis and design, pavement monitoring, evaluation and quality control. She has in excess of 10 years of experience in data collection and analysis for the assessment of pavement structural and functional condition.
Please cite this article as: Plati, C et al., Quantification of skid resistance seasonal variation in asphalt pavements, Journal of Traffic and Transportation Engineering (English Edition), https://doi.org/10.1016/j.jtte.2018.07.003