Effect of climate change on asphalt binder selection for road construction in Italy

Transportation Research Part D 37 (2015) 40–47

Contents lists available at ScienceDirect

Transportation Research Part D journal homepage: www.elsevier.com/locate/trd

Effect of climate change on asphalt binder selection for road construction in Italy Francesco Viola ⇑, Clara Celauro Dipartimento di Ingegneria Civile, Ambientale, Aerospaziale e dei Materiali (D.I.C.A.M.), Università degli Studi di Palermo, Palermo, Italy

a r t i c l e

i n f o

Article history: Available online 16 May 2015 Keywords: Climate change Temperature trend analysis SUPERPAVE

a b s t r a c t This work explores the influence of climate changes on the proper selection of asphalt binder for pavement construction purposes, according to the Performance Grade (PG) defined in the SUPERPAVE specifications. Based on temperature data at national level, it is possible to obtain thematic maps for the whole Italian territory, which is extremely useful for technicians and pavement engineers for selection of asphalt binder for road construction purposes. Furthermore, the statistical significant temperature trends’ knowledge enables deriving thematic maps which allows to include the effects of climate change in the asphalt binder design. It is argued that, due to climate change, the binders to be selected may be different from those commonly selected at the design stage of the infrastructure, since likely higher temperature determine more demanding constraints. The comparison among the PG grades necessary for covering the needs for construction in the different regions of Italy also call for highly performing binders, such as those obtained via specific modification with polymers. This will also imply the need for even more performing materials, in terms of mechanical properties and durability, to be used for modification of neat asphalt as well as the need for defining new specification and testing methods, specifically valid for these modified materials. Ó 2015 Elsevier Ltd. All rights reserved.

Introduction There is a general consensus that the radiative effect of increased atmospheric concentrations of greenhouse gases, caused by human activities, has triggered the rise of global temperature. The latest report of the Intergovernmental Panel on Climate Change (Stocker, 2013) confirms that the climate is changing in ways that cannot be accounted for by natural variability, since human activities have become a dominant force, and are responsible for most of the warming observed over the past 50 years. According to Stocker (2013), over the period 1880–2012, a warming of 0.85 °C has been detected in the globally averaged combined land and ocean surface data as calculated by a linear trend. Several studies confirm this statement at local and continental extent: temperature increases have been recorded in United States (Balling and Idso, 1989; Lettenmaier et al., 1994), Canada (Zhang et al., 2000), Australia (Collins et al., 2000), Southeast Asia and the South Pacific (Manton et al., 2001), China (Zhai and Pan, 2003), India (Kothyari and Singh, 1996), South Africa (Kruger and Shongwe, 2004) and in the Mediterranean area (Piervitali et al., 1997; Sahsamanoglou and Makrogiannis, 1992; Viola et al., 2014). Climate change resulting from the increased greenhouse effect is expected to have great direct implications for hydrological cycle, surface and groundwater resources systems, ecology and indirect consequences on society and economy. Thus ⇑ Corresponding author. E-mail address: [email protected] (F. Viola). http://dx.doi.org/10.1016/j.trd.2015.04.012 1361-9209/Ó 2015 Elsevier Ltd. All rights reserved.

F. Viola, C. Celauro / Transportation Research Part D 37 (2015) 40–47

41

climate change implies a modification of traditional approaches to society control on earth through engineering. For instance, water resources management has to take into account the transient conditions which modify water availability, influenced by rainfall variability, temperature, relative humidity and so on. In the same way, each human engineering practice, directly or indirectly affected by climate forcings, has to take into account the temporal trend of key variables. Construction, maintenance and rehabilitation activities of linear infrastructures, such as road construction, have great impact on the economy of all countries due to the huge amount of human and natural (not renewable) resources involved. Road construction, in particular, is strongly influenced by temperature due to the very large use of temperature susceptible materials such as asphalt concrete (the so-called flexible pavement). The influence of climatic factors on pavement behavior makes the definition of the thermal regime inside the pavement structure of primary importance (Celauro, 2004; Praticò and Vaiana, 2013). The behavior of pavement materials during service life, and so also the behavior of the whole structure, depends on the service temperature and on its cyclical variations (daily and seasonal). The service life of a road infrastructure is typically assumed equal to 20 years after construction: during this service life, maintenance and rehabilitation activities of the pavement are carried out indeed, but the choice of the component materials it typically made at the design stage of the project and the quality of the materials is kept the same, especially for maintenance operations (Praticò et al., 2010). For temperature susceptible materials such as asphalt and asphalt concrete, a long service life cannot be considered as a period where the factors that affect service life of a road are constant, and, therefore, effect of climate change should be adequately taken into consideration already at the design stage, for proper selection of construction materials. A valid aid in proper selection of the adequate binder to be used is given by the SUPERPAVE specifications which have been developed during the Strategic Highway Research Program (SHRP) project (Kennedy et al., 1994), aimed to develop tools and test protocols useful to standardize the use of rheological properties derived from the viscoelasticity theory for classification and identification purposes of the road bitumen. The SUPERPAVE testing protocol has been developed with the intention to be suitable for pure, as well as for modified asphalt binder. Linear viscoelastic behavior is supposed for these materials (and therefore independent from the level of tension or deformation imposed) in order to conveniently simplify their complex behavior, as well as to develop simple and realistic protocols for the industry of the asphalt. Viscoelastic properties of the material are measured at testing temperatures as well as at aging conditions that are representative of the main type of distresses for a road pavement (Petersen et al., 1994). This testing protocol allows one to classify the tested binder in relation to the expected in-service temperature range. Therefore, proper selection of the binder to be used is based on environmental data, traffic level and traffic speed, as known at the time of the design stage. Given these premises, the aims of this paper are double: first obtain a map useful for the choice of the asphalt binder for road construction within the whole Italian area, as already done in other countries by some authors (Aflaki and Tabatabaee, 2009; Al-Abdul Wahhab et al., 1997; Hassan et al., 2008; Khalil et al., 2009; Marciano et al., 1997). At the same time, analyzing temperature time series for trend detection, the second objective of this work is to provide a tool for the selection of the asphalt binder that fulfills the road performance requirements during the 20 year service life even in transient climatic conditions. The Performance Grade Method Based on the SUPERPAVE specifications, the Performance Grade (PG) of a binder is defined in relation to a specific set of temperatures (maximum and minimum, X–Y, respectively) related to the characteristic temperature of the pavement and representative of the extreme climatic conditions in the area of use, once in service. In standard conditions of traffic volume (design traffic <107 ESALs, i.e. 80 kN-Equivalent Single Axle Load) and traffic speed (V > 100 km/h), the characteristics temperature for a surface layer of a flexible pavement are calculated as follows:  X = 98th percentile of T20(max) being X the highest pavement design temperature (°C) and T20(max) the yearly maximum pavement temperature at 20 mm below the surface, over a period of 20 years at least (Kennedy et al., 1994). T20(max) can be simply calculated as a function of the maximum pavement surface temperature, Ts(max), as follows (Huber, 1994):

T 20ðmaxÞ ¼ 0:955T sðmaxÞ  0:8ð CÞ

ð1Þ

In turn, the maximum pavement surface temperature, Ts(max), could be calculated as a function of the air temperature.

T sðmaxÞ ¼ T aðmaxÞ  0:00618u2 þ 0:2289u þ 24:4ð CÞ

ð2Þ

where u is the latitude of the site of the project, expressed in degrees and Ta(max) is obtained selecting, per each year, the maximum value of the 7-day air temperature moving average.  Y = 98th percentile of Ts(min) being Y the lowest pavement design temperature assumed as the 98° percentile of the yearly minimum pavement temperature Ts(min) over a period of 20 years at least. The minimum temperature is calculated on the road surface according to the formula:

42

F. Viola, C. Celauro / Transportation Research Part D 37 (2015) 40–47

T sðminÞ ¼ 0:859T aðminÞ þ 1; 7ð CÞ

ð3Þ

where Ta(min) is the one-day yearly minimum air temperature. Some authors (Bosscher et al., 1998; Lukanen et al., 1998; Mohseni and Carpenter, 2004) questioned the accuracy of the reliability estimates used in temperature model of the current SUPERPAVE recommendations, nevertheless the method still represents a useful and very straightforward tool for comparison purposes when operating a PG zonation of a territory. Seven grades of maximum pavement design temperature X are given and, for each of them, up to seven sub-grades of minimum pavement design temperature, Y, are defined too. Grades and subgrades differ from each other by 6 °C. In the case of traffic speeds or volumes different from those previously defined as ‘‘standard’’, it is required to appropriately increase the class of the PG calculated. In detail, when the traffic volume exceeds 107 ESALs, it is suggested an increase (one grade) in the high temperature grade X. This increase is mandatory for traffic volumes greater than 3  107 ESALs. An increase in the high temperature grade is also considered in the case of low traffic speed: one grade for traffic speed in the range V = 20–70 km/h and two grades for stationary or slow moving traffic (V < 20 km/h). The increase of one grade in the high temperature should be applied for traffic volume or traffic speed, but not for both. Temperature dataset, spatial interpolation and trend detection Daily temperatures recorded in Italy have been provided by the Servizio Meteorologico dell’Aeronautica Militare (Italian Air Force Meteorological Service) for the period 1984–2013 (30 years). A total of 71 weather stations have been studied, localized over an area of about 300,000 km2. The selection of these weather stations has been carried out in order to guarantee a good representation of the whole national territory as well as its orography (therefore, special attention has been paid to the elevation above the sea level of the selected stations). A summary of the weather stations’ characteristics is provided in Table 1. The annual maxima and minima have been spatial interpolated using the method illustrated by Di Piazza et al. (2011) which uses firstly the derivation of a linear regression equation between temperature and elevation. This relationship is applied to each point of the Italian geographic area providing a ‘‘first guess’’ of temperature and, for the gauged sites residuals (i.e. errors equal to the difference between observed and estimated temperature). These residuals exhibit a strong spatial correlation, which allows their interpolation using the Ordinary Kriging. The sum of these interpolated residuals and the ‘‘first guess’’ (i.e. linear regression with elevation) provided, for each point of the Italian territory, the expected value of temperature. High and low pavement design temperatures have been calculated using historical data for each station considered, according to the previously described PG Grading System. Table 1 provides the results calculated over the period 1984–2013. Fig. 1a shows the spatial distribution of X during the period of analysis, i.e. the contours of the highest pavement design temperature. Obviously, the highest pavement temperature values (X = 64 °C) are located along the Southern coast, while the lowest values (X = 46 °C) characterize higher elevations in the Alps. Fig. 1b shows the spatial distribution of Y and again the highest values (Y = 10 °C) are located along the Southern coast, while the lowest values (Y = 28 °C) characterize higher elevations in the Alps. Both Fig. 1a and b refer to the current climate (2013 scenario), as result from mean historical values. Temperature trend detection within the Italian temperature database Several statistical procedures can be used for trends detection, in particular parametric and non-parametric tests. In this study, the non-parametric Mann–Kendall test for trend detection (Kendall, 1962; Mann, 1945) has been used. This test identifies the presence of a trend, without making an assumption about the distribution properties. Moreover, non-parametric methods are less influenced by the presence of outliers. In a trend test, the null hypothesis H0 is that there is no trend in the population from which the data is drawn, while hypothesis H1 is that there is a trend in the records. The test statistic, Kendall’s S (Kendall, 1962), is calculated as:

S¼

n1 X n X

signðxj  xi Þ

ð4Þ

i¼1 j¼iþ1

where xi and xj are the data values at times i and j, n is the length of the dataset and

8 > < 1 if ðxj  xi Þ > 0 0 if ðxj  xi Þ ¼ 0 signðxj  xi Þ ¼ > : 1 if ðxj  xi Þ < 0

ð5Þ

Under the null hypothesis that xi are independent and randomly ordered, the statistic S is approximately normally distributed when n P 8, with zero mean and variance as follows:

r2 ¼

nðn  1Þð2n þ 5Þ 18

The standardized test statistic ZS is computed by:

ð6Þ

43

F. Viola, C. Celauro / Transportation Research Part D 37 (2015) 40–47 Table 1 Considered temperature stations, coordinates and elevation. Pavement design temperatures in the two considered scenarios (2013 and 2033). Station

Alghero Arezzo Bari Palese Macchie Bergamo Orio al Serio Bolzano Bonifati Borgo Panigale Bologna Brescia Ghedi Brindisi Cagliari Elmas Campobasso Capo Frasca Capo Mele Capo Palinuro Catania Sigonella Cozzo Spadaro Crotone Dobbiaco Enna Falconara Firenze Peretola Foggia Amendola Fonni Frontone Frosinone Gela Genova Sestri Grazzanise Grosseto Guardiavecchia Guidonia Latronico Lecce Luni Sarzana Marina di Ginosa Messina Milano Linate Monte Bisbino Monte Cimone Monte Santangelo Monte Scuro Monte Terminillo Novara Cameri Paganella Palermo Passo della Cisa Pescara Piacenza Pian Rosa Pisa Potenza Pratica di Mare Prizzi Punta Marina Radicofani Reggio Calabria Rimini San Valentino alla Muta Sant’Egidio Perugia Tarvisio Termoli Torino Bric della Croce Torino Caselle Trapani Birgi Treviso Treviso Istrana Udine Rivolto

Latitude

40°330 N 43°280 N 41°070 N 45°400 N 46°290 N 39°350 N 44°310 N 45°240 N 40°370 N 39°160 N 41°330 N 39°150 N 43°570 N 40°010 N 37°260 N 36°400 N 39°040 N 46°440 N 37°330 N 43°360 N 43°470 N 41°310 N 40°070 N 43°300 N 41°380 N 37°040 N 44°170 N 41°050 N 42°450 N 41°130 N 41°590 N 40°050 N 40°210 N 44°040 N 40°250 N 38°110 N 45°260 N 45°520 N 44°110 N 41°420 N 40°120 N 42°290 N 45°290 N 46°100 N 38°060 N 44°280 N 42°270 N 45°020 N 45°550 N 43°430 N 40°380 N 41°390 N 37°430 N 44°260 N 42°530 N 38°060 N 44°030 N 46°450 N 43°060 N 46°300 N 42°000 N 45°020 N 45°040 N 37°540 N 41°020 N 45°400 N 45°570 N

Longitude

8°190 E 11°520 E 16°520 E 9°410 E 11°210 E 15°530 E 11°160 E 10°160 E 17°560 E 9°020 E 14°390 E 9°070 E 8°100 E 15°160 E 14°580 E 15°070 E 17°070 E 12°130 E 14°160 E 13°210 E 11°110 E 15°460 E 9°150 E 12°440 E 13°200 E 14°140 E 9°250 E 14°060 E 11°060 E 9°240 E 12°420 E 16°000 E 18°100 E 10°000 E 16°520 E 15°330 E 9°160 E 9°030 E 10°420 E 15°580 E 16°400 E 13°000 E 8°390 E 11°040 E 13°210 E 9°550 E 14°120 E 9°410 E 7°440 E 10°240 E 15°480 E 12°280 E 13°250 E 12°170 E 11°460 E 15°380 E 12°330 E 10°320 E 12°290 E 13°340 E 14°590 E 7°440 E 7°410 E 12°290 E 15°130 E 12°050 E 12°590 E

Elevation (m)

20.00 277.00 10.00 141.00 1263.00 295.00 172.00 60.00 7.00 22.00 600.00 80.00 2.00 127.00 22.00 27.00 9.00 2114.00 530.00 15.00 323.00 34.00 1252.00 627.00 249.00 10.00 6.00 12.00 8.00 45.00 252.00 536.00 48.00 18.00 0.00 33.00 128.00 643.00 1713.00 317.00 14.00 1369.00 147.00 1593.00 27.00 694.00 1.00 57.00 3660.00 7.00 731.00 83.00 688.00 0.00 501.00 49.00 6.00 999.00 257.00 986.00 42.00 385.00 502.00 9.00 729.00 20.00 39.00

2013

2033

X

Y

X

Y

58 58 58 58 58 58 58 58 58 64 58 58 58 58 46 58 58 52 58 58 58 64 58 58 64 58 58 58 58 58 64 58 64 58 58 58 58 46 46 58 52 52 58 46 58 52 58 58 46 58 58 58 58 58 58 64 58 52 58 52 58 52 58 58 52 58 58

10 16 10 16 16 10 10 16 10 10 10 10 10 10 10 10 10 22 10 10 16 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 16 16 22 10 16 22 16 22 10 16 10 16 28 10 10 10 10 10 10 10 10 22 16 22 10 10 10 10 10 16 10

64 64 58 64 58 58 64 58 58 64 58 58 58 58 46 64 58 52 58 52 58 64 58 58 64 64 58 58 64 58 64 58 64 58 64 64 64 46 46 64 52 52 58 46 64 52 52 64 46 58 64 58 58 64 58 64 58 52 64 58 58 58 58 64 58 64 64

10 16 10 16 10 10 10 16 10 10 10 10 10 10 10 10 10 22 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 16 22 10 16 22 10 22 10 16 10 10 28 10 10 10 10 10 10 10 10 22 10 16 10 10 10 10 10 16 16

(continued on next page)

44

F. Viola, C. Celauro / Transportation Research Part D 37 (2015) 40–47

Table 1 (continued) Station

Verona Villafranca Vicenza Viterbo Volterra

Latitude

0

45°21 N 45°320 N 42°250 N 43°230 N

8 S1 > < r if S > 0 if S ¼ 0 ZS ¼ 0 > : Sþ1 if S < 0 r

Longitude

0

10°50 E 11°320 E 12°060 E 10°510 E

Elevation (m)

45.00 36.00 345.00 141.00

2013

2033

X

Y

X

Y

58 58 58 58

16 16 10 10

64 64 64 58

10 16 10 10

ð7Þ

and compared with a standard normal distribution at the required level of significance. In this analysis confidence level 95% has been considered (a = 0.05), thus the null hypothesis is verified when |Zs| < 1.96. A positive value of ZS indicates an increasing trend and vice versa. The magnitude of trends was evaluated using a non-parametric robust estimate determined by Hirsch et al. (1982):

  xj  xl b ¼ Median 8l < j jl

ð8Þ

where xl is the l-th observation antecedent to the j-th observation xj. Statistical significance of trends has been evaluated for the variables X and Y in each of the considered weather station. Fig. 1c and d illustrate the results of this analysis, showing both the presence of significant trends (black dots) and not significant ones (circles). At each location also the magnitude of the tendency has been evaluated. This allowed to extrapolate the future values of the considered variables using simple linear functions. Forecasted local values of X and Y (named X_2033 and Y_2033) have been then spatially interpolated using the same technique illustrated in paragraph 3, obtaining the spatial description of these variables depicted in Fig. 1c and d. These latter may be considered a reliable projection of climatic constraints for road construction in the next 20 years. The choice of using statistical significant trends extrapolation instead of GCMs outputs has been done because the spatial resolution of the latters is too coarse to be used directly in local studies. At the pixel scale, which ranges from 130 to 550 km, the elevation influence, which is crucial in determining maximum and minimum annual air temperature and consequently the bitumen design parameters, is of course neglected. Based on the X and Y contours depicted in Fig. 1, it is straightforward to calculate the percentage of national area characterized by a specific value of X and Y, for the current and for the future scenario, as summarized in Table 2. It is worth of consideration the comparison between current and future conditions, because it may reveal jumps between grades within the Performance Grade method. This analysis has been carried out by a map algebra calculation. Differences between future (i.e. 2033) and current (i.e. 2013) values of X and Y have been obtained locally, thus allowing to highlight if predictable PG grades of bitumen to be selected for a road pavement in any area of the Italian territory remain the same during the service life of the pavement or not, due to climatic alterations. Outputs are depicted in Fig. 2a and b where PG grades switches have been spatially mapped. In particular, from Fig. 2a, it can be noticed that the high temperature X and has a noticeable increase of 1 grade, over the 27% of the Italian territory. From Fig. 2b one can conclude that also the low temperature Y has a noticeable change, due to the climatic change over the design period, since a different grade is obtained for about the 15% of the territory. Nevertheless, for 12% of the case, an increase in the minimum design temperature is estimated, while for the remaining cases a decrease is obtained, mainly located in the Venice Gulf and in the Western Alps. Results and discussion Based on the results obtained, one can conclude that, with regards to the climatic data of last three decades, PG 64-22 binder proves to be the most common grade since it covers about the 55% of the Italian territory. Although it seems from Table 1 that eight different PG binders are needed to fully cover the for the different needs of the various climate regions of Italy (and at least one or two more may be needed, depending on the heavy traffic volumes and/or on the traffic speed), it is also clear that just four PG binders cover about the 90% of the range of climate conditions (namely, these are PG 58-16, 58-22, 64-16 and 64-22 and they do not require polymer modification). This allows to simplify, with the consequent economical and industrial benefit for asphalt suppliers and hot mix producers, the range of asphalt binders to be supplied, also because few modifications in manufacturing processes of the asphalt sources typically employed in Italy may ensure consistent production of the whole range. As far as the scenario 2033 is considered, again the PG 64-22 is the most common grade and it covers about the 60% of the needs for Italy, while again the first four binder cover the 90% of the range of climate conditions. The substantial difference is that, due to climate change, a significant shift of PG towards highest design temperatures is obtained. While the PG 64-28 was not even considered in the scenario 2013, climatic data forecasted a need of it for the 10% of the whole territory (again, the case of PG shift or bump, due to traffic speeds and volume, is not taken into consideration, in this case). For this specific

F. Viola, C. Celauro / Transportation Research Part D 37 (2015) 40–47

45

Fig. 1. High temperature grade contours, X, in the scenario 2013 (a) and 2033 (c). Low temperature grade contours, Y, in the scenario 2013 (b) and 2033 (d). Significant trends are represented with black dots, not significant ones with open circles.

46

F. Viola, C. Celauro / Transportation Research Part D 37 (2015) 40–47 Table 2 Percentage of national area covered by different PG grades in the two considered scenarios. X 46

52

58

64

Y_2013 10 16 22 28

1.8 0.0 0.0 0.0

4.4 2.5 0.0 0.0

1.9 11.6 14.6 0.0

0.0 7.9 55.3 0.0

Y_2033 10 16 22 28

1.4 0.0 0.0 0.0

2.9 3.3 0.0 0.0

0.6 7.3 10.7 1.2

0.0 2.3 59.6 10.6

Fig. 2. Shift in the high temperature (a) and in the low temperature (b) grade between the two considered scenarios.

grade, depending on the source asphalt as available at local level, modification with polymer may be necessary in order to meet the requirements as specified in Table 1 (modification, in fact, is proved to widen the range between the binder’s highand low temperature grades). This implies the availability of specific production plants, increased cost of components and, therefore, of final products with a cost per ton of a modified binder even doubles that of the neat binder (and a consequent increase in the final cost of the asphalt concrete, once in place). So far, use of modified binder is mainly limited for specific road which experience heavy traffic or standing/slow moving load. Based on the PG grades summarized in Table 1, modified binder should be considered in the future for ordinary application too, due to the detected trend of changes in the climate conditions in a medium-long term scenario as the one typically assumed as service life for a road infrastructure. This brings the research community to deepen the research activities in the field of asphalt modification for enhancing the overall performance of asphalt binder, as well as for improving the consequent manufacturing processes, mix plant operations or laydown procedures, in order to meet the increasing need for high

F. Viola, C. Celauro / Transportation Research Part D 37 (2015) 40–47

47

performing asphalt mixtures (Yildirim, 2007). Additionally, new specification and testing methods, specifically valid for these innovative materials, will challenge the technologist’s work in the next future. This also brings to recommend the selection of temperature susceptible materials such as asphalt binder for pavement purposes to be carried out taking into consideration the effect of climate change over the whole service life of the pavement. Indeed, accurate validation of the SUPERPAVE pavement temperature algorithm (based on temperature data analyses) and the binder specification limits is necessary, from meteorological data of the territory to be zoned. Acknowledgments The authors wish to thank Giorgio Di Pisa for maps editing and Giuseppe Parla for his contribution to the statistical analysis. References Aflaki, S., Tabatabaee, N., 2009. Proposals for modification of Iranian bitumen to meet the climatic requirements of Iran. Constr. Build. Mater. 23 (6), 2141– 2150. Al-Abdul Wahhab, H.I., Asi, I.M., Al-Dubabe, I.A., Ali, M.F., 1997. Development of performance-based bitumen specifications for the Gulf countries. Constr. Build. Mater. 11 (1), 15–22. Balling, R.C., Idso, S.B., 1989. Historical temperature trends in the United-States and the effect of urban-population growth. J. Geophys. Res.-Atmos. 94 (D3), 3359–3363. Bosscher, P.J., Bahia, H.U., Thomas, S., Russell, J.S., 1998. Relationship between pavement temperature and weather data: wisconsin field study to verify superpave algorithm. Transport. Res. Rec.: J. Transport. Res. Board 1609 (1), 1–11. Celauro, C., 2004. Influence of the hourly variation of temperature on the estimation of fatigue damage and rutting in flexible pavement design. Int. J. Pavement Eng. 5 (4), 221–231. Collins, D.A., Della-Marta, P.M., Plummer, N., Trewin, B.C., 2000. Trends in annual frequencies of extreme temperature events in Australia. Aust. Meteorol. Mag. 49 (4), 277–292. Di Piazza, A., Conti, F.L., Noto, L.V., Viola, F., La Loggia, G., 2011. Comparative analysis of different techniques for spatial interpolation of rainfall data to create a serially complete monthly time series of precipitation for Sicily, Italy. Int. J. Appl. Earth Observ. Geoinform. 13 (3), 396–408. Hassan, H.F., Al-Nuaimi, A., Al-Oraimi, S., Jafar, T.M.A., 2008. Development of asphalt binder performance grades for Omani climate. Constr. Build. Mater. 22 (8), 1684–1690. Hirsch, R.M., Slack, J.R., Smith, R.A., 1982. Techniques of trend analysis for monthly water quality data. Water Resour. Res. 319, 83–95. Huber, G.A., 1994. Weather Database for the SUPERPAVE [Registered Trademark] Mix Design System. Strategic Highway Research Program, National Research Council. Kendall, M.G., 1962. Rank Correlation Methods. Hafner Publishing Company, New York. Kennedy, T.W., Huber, G.A., Harrigan, E.T., Cominsky, R.J., Hughes, C.S., Von Quintus, H., Moulthrop, J.S., 1994. Superior Performing Asphalt Pavements (Superpave): The Product of the SHRP Asphalt Research Program. Strategic Highway Research Program, National Research Council. Khalil, N., Iaaly-Sankari, A., Jadayel, O., 2009. Temperature Based GIS Approach For Asphalt Binder Specifications in Lebanon, ESRI International User Conference, San Diego, USA. Kothyari, U.C., Singh, V.P., 1996. Rainfall and temperature trends in India. Hydrol. Process. 10 (3), 357–372. Kruger, A.C., Shongwe, S., 2004. Temperature trends in South Africa: 1960–2003. Int. J. Climatol. 24 (15), 1929–1945. Lettenmaier, D.P., Wood, E.F., Wallis, J.R., 1994. Hydro-climatological trends in the continental United-States, 1948–88. J. Clim. 7 (4), 586–607. Lukanen, E.O., Han, C., Skok, E.L., 1998. Probabilistic method of asphalt binder selection based on pavement temperature. Transport. Res. Rec.: J. Transport. Res. Board 1609 (1), 12–20. Mann, H.B., 1945. Non parametric tests again trend. Econometrica 13, 245–259. Manton, M.J., Della-Marta, P.M., Haylock, M.R., Hennessy, K.J., Nicholls, N., Chambers, L.E., Collins, D.A., Daw, G., Finet, A., Gunawan, D., Inape, K., Isobe, H., Kestin, T.S., Lefale, P., Leyu, C.H., Lwin, T., Maitrepierre, L., Ouprasitwong, N., Page, C.M., Pahalad, J., Plummer, N., Salinger, M.J., Suppiah, R., Tran, V.L., Trewin, B., Tibig, I., Yee, D., 2001. Trends in extreme daily rainfall and temperature in Southeast Asia and the South Pacific: 1961–1998. Int. J. Climatol. 21 (3), 269–284. Marciano, Y., Bassez, O., Ferraro Maia, A., Le Clerc, S., 1997. Les specifications SUPERPAVE: une contribution shell au’’ decryptage’’-carte de France des PG (performance grade). Revue Generale Des Routes-Voirie-Equipements-Autoroutes-Aeroports (748). Mohseni, A., Carpenter, S., 2004. Development of enhanced algorithm for Superpave High Temperature Performance Grade (PG) Selection and New Software Version. Long-Term Pavement Performance Group, FHWA. Petersen, J.C., Robertson, R., Branthaver, J., Harnsberger, P., Duvall, J., Kim, S., Anderson, D., Christiansen, D., Bahia, H., 1994. Binder Characterization and Evaluation, vol. 1, p. 56. Piervitali, E., Colacino, M., Conte, M., 1997. Signals of climatic change in the central-western Mediterranean basin. Theor. Appl. Climatol. 58 (3–4), 211–219. Praticò, F., Vaiana, R., 2013. A study on volumetric versus surface properties of wearing courses. Constr. Build. Mater. 38, 766–775. Praticò, F., Casciano, A., Tramontana, D., 2010. Pavement life-cycle cost and asphalt binder quality: theoretical and experimental investigation. J. Constr. Eng. Manage. 137 (2), 99–107. Sahsamanoglou, H.S., Makrogiannis, T.J., 1992. Temperature trends over the mediterranean region, 1950–88. Theor. Appl. Climatol. 45 (3), 183–192. Stocker, D.Q., 2013. Climate Change 2013: The Physical Science Basis. Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Summary for Policymakers, IPCC. Viola, F., Liuzzo, L., Noto, L.V., Lo Conti, F., La Loggia, G., 2014. Spatial distribution of temperature trends in Sicily. Int. J. Climatol. 34 (1), 1–17. Yildirim, Y., 2007. Polymer modified asphalt binders. Constr. Build. Mater. 21 (1), 66–72. Zhai, P.M., Pan, X.H., 2003. Trends in temperature extremes during 1951–1999 in China. Geophys. Res. Lett. 30 (17). Zhang, X.B., Vincent, L.A., Hogg, W.D., Niitsoo, A., 2000. Temperature and precipitation trends in Canada during the 20th century. Atmos.-Ocean 38 (3), 395– 429.