Pavement Temperature Contours

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Journal of The Institution ofEngineers (India): Series ACivil, Architectural, Environmental andAgricultural Engineering ISSN 2250-2149Volume 95Number 2 J. Inst. Eng. India Ser. A (2014) 95:83-90DOI 10.1007/s40030-014-0074-y

Development of Pavement TemperatureContours for India

M.R.Nivitha & J.M.Krishnan

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ORIGINAL CONTRIBUTION

Development of Pavement Temperature Contours for India

M. R. Nivitha J. M. Krishnan

Received: 22 February 2013 / Accepted: 5 May 2014 / Published online: 3 June 2014

The Institution of Engineers (India) 2014

Abstract The stress-strain response of the bituminous

pavements is highly sensitive to temperature. To system-

atically analyze the pavement performance, it is necessary

that one understands the variation of pavement temperature

spatially and temporally during the life time of a pavement.

In this investigation, historic air temperature data for 37

locations across India was collected. Using this database,

pavement temperature data was predicted by an appropriate

air temperature-pavement temperature model. High and

low temperature pavement temperature contours were

generated for the first time for India. It was seen that the

locations spanning from Srinagar to Madhya Pradesh and

Rajasthan to Orissa were extremely critical. The minimum

temperature in these locations was 10 C and the maxi-mum temperature was around 68 C. Clearly such infor-mation is necessary when making choice of binder grade

and bituminous layer thickness.

Keywords Pavement temperature Artificial neural networks Air temperature forecasting Design air temperature

Introduction

Bitumen is a complex construction material used for

pavement construction. Of the total pavements constructed

across the world, 90 % of them use bitumen as the binder.

Though the proportion of binder is only around 46 % in

the bituminous layers, it has a large influence on the dis-

tresses in bituminous pavements.

The three common distresses observed in a bituminous

pavement are rutting, fatigue cracking and low temperature

cracking. The properties of binder chosen is considered to

have a significant influence on these three distresses [1].

Rutting, fatigue cracking and low temperature cracking

observed in a pavement are influenced by the binder

approximately to an extent of 80, 60 and 40 % respectively

[2]. Hence the type of binder chosen for any given location

is a significant factor affecting the performance of the

pavement.

The critical factors influencing the choice of binder for

any pavement are pavement temperature and traffic. Binder

response ranges from elastic solid to viscoelastic solid to

viscoelastic fluid to non-Newtonian and Newtonian

depending on the temperature it is subjected to. In the

working temperature ranges of a pavement (1065 C),bitumen exhibits predominantly viscoelastic behaviour and

hence the mechanical response is dependent on speed of

traffic (rate of loading). Owing to this effect of temperature

and traffic, the required performance of a binder can be

obtained only in a certain temperature and traffic range.

Hence it is essential to use the binder at locations which

have identical temperature and traffic factors. To enable

this, temperature and traffic details are required to be

known for any location before selecting the appropriate

type of binder.

The importance of temperature and traffic on the per-

formance of the pavement is understood and the pavement

design specifications proposed by various countries

account for these factors while selecting an appropriate

grade of binder. In India, the design guidelines for

M. R. Nivitha J. M. Krishnan (&)Department of Civil Engineering, Indian Institute of Technology

Madras, Chennai 600036, India

e-mail: [email protected]

123

J. Inst. Eng. India Ser. A (AprilJune 2014) 95(2):8390

DOI 10.1007/s40030-014-0074-y

Author's personal copy

bituminous pavements [3] gives a very general approach

regarding the choice of binder. A broad classification is

provided for temperature and traffic and the binder grade is

specified depending upon the combination of these two

factors. The climatic regions of the country are classified as

hot, cold and moderate and the traffic is considered in two

categories namely high and low volume. The Australian

specifications [4] use the same baseline except that quan-

titative values are given for temperature and traffic.

The Superpave method of binder selection [2] currently

being followed in North America encompasses detailed

analysis on the influence of temperature and traffic on the

performance of pavements. The Superpave method of

binder specification selects the grade of binder based on

temperature of the location where it is to be used and tests

the binder for its performance properties with respect to the

temperature range prevalent at the selected location. Seven

day average maximum and one day minimum pavement

temperatures are considered as the design pavement tem-

peratures. Traffic is included in the specification by means

of testing the binder properties at a specified frequency.

The binder is tested for a constant traffic volume of 10

Million Standard Axles (MSA) and traffic speed of

80 kmph. For other traffic conditions, suitable modification

procedures are specified [2]. Thus the final grade of binder

can be obtained for a given location having a specified

traffic speed and volume.

To develop a robust binder selection methodology cus-

tomized to the climatic and traffic conditions of any region,

one needs meticulous data collection. This is the major

limitation as far as India is considered. The basic infor-

mation required for binder selection and stress-strain ana-

lysis is the data pertaining to the pavement temperature. To

the best of the knowledge of the authors, such data was

collected by the Highways Research Station (HRS) in

Chennai as early as 1969 [5]. In this paper, an attempt is

made to use the available air temperature data and statis-

tical models to generate pavement temperature data for

India.

Data Collection

Thirty seven locations were selected such that they cater to

all the geographical areas of India. Daily maximum and

minimum air temperature data were collected for these

locations, for a period of 30 years (19702000), from the

archived database available at Indian Meteorological

Department, Pune. A design period of 20 years was chosen

during which air temperature and pavement temperature

has to be predicted using the 30 years historical weather

data. Hence the window here is a total period of 50 years

and the yearly design air temperature pattern is expected to

vary in the 50 year period. This difference is expected to be

amplified to a significant level when it is converted into

pavement temperature. It is well known that the air tem-

perature follows periodic variations with varying cycle

times. Some predominant air temperature patterns are the

Bruckners cycle (around 35 years) [6] and Hales cycle

(around 8 years) [7]. Forecasting air temperature using

pattern recognition tools can capture the pattern in the air

temperature history data and reflect the same for the design

period. This to an extent will provide the realistic daily air

temperature for the design period.

For the thirty seven chosen locations and for a design

period of thirty years, a total number of 8,10,300 data

points were collected. Of the total 14,600 data points

available for each city, at least 50 data points were missing.

The various methods available to fill in missing entries may

be categorized under three heads: within-station, regres-

sion-based and inter-station [8]. Using the methodology for

within-station category, the average of the past five days

and the next 5 days was taken as the current days tem-

perature for filling in missing and anomalous data. This can

be expressed as follows:

xi P5

n1 xin P5

n1 xin

2

1

where, xi = Air temperature of the missing entry

As a check to validate this approach, 1,000 air temper-

ature data points were selected from the input data for

Chennai and 10 temperature values starting from 1st Jan-

uary, 1970 were removed at an interval of 50 data points.

These missing values were calculated using Eq. 1. The

average mean absolute error(MAE) of the selected points

was 1.032 C and MAE up to 2 C is considered acceptablefor filling in missing data in the literature [9]. Hence this

method was considered acceptable and it was used to fill in

all the missing entries. There were also cases when the data

was missing for more than two consecutive days. In these

cases, recursive iteration was carried out on the basis that,

the difference in values between two successive iterations

was less than 0.001.

Air Temperature Forecast

Among the various models available to forecast time series,

ANN was chosen to predict the air temperature as it is

considered to be effective in pattern recognition and long

term forecasting [10]. The in-built Neural Networks tool

box available in MATLAB [11] was used to predict the air

temperature and for that purpose the available data was

separated into training, testing and validation sets. All the

initial trials were carried out for Chennai data by varying

84 J. Inst. Eng. India Ser. A (AprilJune 2014) 95(2):8390

123


the number of layers, number of neurons in each layer and

the proportion of training and testing data to obtain the best

fit parameters for predicting the air temperature. As an

initial trial, 15 years (19701985) data was taken as

training data, 10 years (19861995) for testing and five

years (19962000) for validation. The number of layers

were varied from 2 to 5 and the neurons in each layer were

varied from 1 to 20. The best fit was obtained when 2

layers were used having 20 and 2 neurons respectively and

a proportion of 20:7:3 for training:testing:validation data

set were used to forecast the air temperature. The same best

fit parameters were used for all the 37 cities and for each

city ANN was trained separately with daily maximum and

minimum air temperatures to predict the same for the

design period.

To validate the ANN model, daily air temperature data

for Chennai, available at an internet domain [12] was

collected for the year 2009 and compared with the pre-

dicted air temperature (Fig. 1). The MAE for daily maxi-

mum air temperature is 1.88 C and it is slightly higherthan the daily minimum air temperature having a MAE of

1.35 C. An MAE up to 2 C is commonly reported inliterature [13, 14] for air temperature forecasting using

ANN and hence the MAE obtained in this study is con-

sidered to be acceptable. The yearly maximum and mini-

mum air temperature for the 50 year period comprising the

data collection period (19702000) and the design period

(20012020) for Chennai is shown in Fig. 2. It can be

observed from Fig. 2 that the variation of design air tem-

perature for the 50 years is within a band of 5 C with thevariation of air temperature more pronounced in the actual

data compared to the forecast.

The next step after obtaining the daily air temperatures

for the design period would be to choose a particular air

temperature or a norm of air temperature values to

represent the temperature variation in a year for the

selected location. The temperature variation of a location is

commonly indicated in terms of the average or maximum

and minimum air temperatures experienced at the location.

In India, the design guidelines for bituminous pavements

[3] considers the annual average pavement temperature

directly for determining the material properties for stress-

strain analysis. This measure of temperature does not

highlight the extremes of temperature a pavement is

experiencing in its service life. It is expected that locations

having the same annual average pavement temperature can

have different maximum and minimum temperatures and

hence different susceptibility to rutting and cracking. It is

thus necessary to consider the yearly maximum and mini-

mum temperatures in a binder selection methodology. In

this paper, the seven day average maximum and one day

minimum air temperatures as per the Superpave specifi-

cation [2] is followed to calculate the design air tempera-

ture. These approaches also have their own limitations

[15].

Pavement Temperature Model

The design pavement temperatures can be calculated from

design air temperatures using analytical models and

regression equations. In the analytical models, the heat

equation is solved and the pavement temperature is cal-

culated knowing the weather parameters such as solar

radiation, absorptivity and emissivity of the surface and air

temperature [16]. Regression equation developed from the

measured pavement temperature database relating air

temperature and latitude can also be used and the pavement

temperature for the required time period can be calculated

[17]. Due to the complexity in measuring the material

Air

tem

pera

ture

, C

Fig. 1 Actual and predicted (using ANN) air temperature for Chennaifor the year 2009

Air

tem

pera

ture

, C

Fig. 2 Variation of yearly maximum and minimum air temperaturesfor Chennai from 1970 to 2020

J. Inst. Eng. India Ser. A (AprilJune 2014) 95(2):8390 85

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parameters required for use in the analytical models, most

of the attempts related to pavement temperature have

focused on regression models. The various factors influ-

encing the pavement temperature are air temperature, lat-

itude, solar radiation, wind speed and rainfall. Among these

factors, air temperature and latitude are considered as the

sole factors influencing the pavement temperature [17].

The development of regression models require consid-

erable amount of data and since such data was not available

for India, the air temperature and the corresponding

pavement temperatures data collected as part of the Long

Term Pavement Performance Program (LTPP) of USA was

used to develop the regression model [18]. The LTPP

database [18] monitors and collects data for 890 sites under

various studies such as General Pavement Section (GPS),

Specific Pavement Study (SPS) and Seasonal Monitoring

Program (SMP). The SMP, which collects the various data

including the air and pavement temperature for a location,

has been implemented only in 66 sites. Eleven sites (Table

1) out of the sixty six sites which had similar latitude and

altitude as that of India were selected on the basis that

locations having similar latitude and altitude are expected

to have identical climatic conditions. The southern part of

the USA and the northern part of India have similar latitude

(36 to 25N) (Table 1) and hence are expected to haveidentical climatic conditions for specific altitude ranges. To

verify the similarity in air temperature pattern, the monthly

average air temperatures of a location in India (Patiala,

having a latitude of 30.33N and an altitude of 249 m) wascompared with another in USA (Atlanta (SHRP ID:1031),

having a latitude of 32.61N and an altitude of 138 m). Itcan be observed from Fig. 3 that the air temperature pattern

is similar for both the locations and hence details from

LTPP can be used for the regression model. The altitude of

the selected locations in India and USA was limited to less

than 2000 m. A total of 172 data points were collected and

the data collection period was from 1994 to 2001. The

database had data points from all the months throughout

the year. To formulate a regression equation using this

database, a linear regression equation with two variables

was assumed considering the effect of latitude and air

temperature on the pavement temperature [17]. The coef-

ficients of the equation were determined by the in-built

linear regression function in MATLAB and given as

follows:

Pt 0:7147 1:3023At 0:1103L; 2where, Pt = pavement temperature (

C); At = air temper-ature (C); L = latitude of the selected location. Thisregression model developed is used to calculate pavement

temperature for the entire country.

As there was no pavement temperature information

available to validate the regression model, a comparison

Air

tem

pera

ture

, C

Fig. 3 Comparison of air temperature for Atlanta and Patiala for theyear 2000

Table 1 Details of locations selected for collection of pavement temperature from LTPP database [18]

United States of America India

SHRP ID Latitude Altitude, m Location Latitude Altitude, m

1024 35.27 1663 Guwahati 26.11 47

1053 38.69 1567 Gwalior 26.14 205

1005 32.61 138 Jodhpur 26.17 217

1031 34.40 37 Gorakhpur 26.45 76

1112 34.30 1146 Jaipur 26.53 385

1060 28.51 24 Lucknow 26.55 122

1068 33.50 136 Dibrugarh 27.29 110

1077 34.54 559 Delhi 28.37 233

1122 29.23 143 Patiala 30.2 249

3739 26.98 11 Dharamshala 32.16 1457

1001 37.28 1336 Srinagar 34.08 1585


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was performed with the data points extracted from the HRS

data [5]. This study was carried out in the year 19691971.

The highways research station at Chennai constructed an

experimental bituminous concrete section and collected

pavement temperature at the surface of the pavement as

well as at different depths. From this report, 48 data points

were extracted to validate the regression model developed

as a part of this study. Among these, 24 data points were

hourly pavement temperature measurements on a single

day, 8th June, 1969 and the other 24 points were monthly

average pavement temperatures for two years, 1970 and

1971. Figure 4 shows the predictions for the full 24 h. Due

to brevity, the monthly average predictions are not shown

here. It is seen that the present model can predict the

pavement temperature to a reasonable accuracy.

Using Eq. 2, the design maximum and minimum

pavement temperatures were calculated for all the selec-

ted 37 locations. The design pavement temperature was

calculated using the maximum of seven day average

maximum and minimum of one day minimum air tem-

perature obtained for the design period [2]. The reliability

considered in this case is of the order of 99.6 % as the

maximum of seven day average maximum air temperature

was used in calculating the pavement temperature and

similarly the one day minimum air temperature for the

design minimum case. However, in circumstances where

agencies specify other reliability levels, the design air

temperature can be considered accordingly. The mean and

standard deviation values were calculated for design

maximum and minimum air temperatures for all the cities,

as represented in Table 2. One can now use these values

to calculate the design air temperatures for any required

level of reliability and convert them into pavement tem-

peratures using Eq. 2. The maximum and minimum

pavement temperatures calculated for reliability levels of

99 and 75% are shown in Table 2.

Pavement Temperature Contours

Quantum GIS software was used to interpolate and

determine the spatial distribution of pavement tempera-

tures. Pavement temperature values were input for the

selected 37 locations and temperature values for the

intermediate locations were interpolated using triangular

interpolation. The contour maps were then generated for

the entire country based on the interpolated values for all

the locations. The entire map area was divided into 1,000

grids vertically and horizontally and the values were

interpolated for each cell. As a check to validate the

interpolation method inbuilt in the software, three loca-

tions namely Bhubaneswar, Coimbatore and Kurnool

were removed from the data set and the pavement tem-

peratures were determined based on interpolation. The

average MAE was 0.59 C for design maximum tem-perature and 1.35 C for design minimum temperaturewere obtained for the interpolated values. The maximum

deviation obtained for these cities was less than 2 C formaximum and minimum pavement temperatures. The

maximum pavement temperature contour for India is

shown in Fig. 5 for every two degree increase in pave-

ment temperature starting from 54 to 68 C. For anylocation lying in between the contours, one can interpo-

late between the two temperature contours.

It can be deduced that the pavement temperatures in

India are highest in regions of central Rajasthan, portions

of Haryana, Uttar Pradesh, Madhya Pradesh, Bihar,

Jharkhand and Chhattisgarh. In these locations, the pave-

ment temperature crosses 65 C. The design minimumpavement temperature in some of these same locations

goes up to 10 C making them critical both in terms ofrutting and low temperature cracking. Hence the binder to

be used in these locations is required to have excellent

temperature susceptibility to withstand both rutting and

low temperature cracking. The core of the southern part of

India experiences high temperature during summer over 65C but the low temperature does not fall below 18 Ccompared to the northern part where the low temperature is

observed to be 10 C. In the coastal regions of thesouthern part of India, the high temperature does not rise

beyond 60 C and the low temperature does not fall below10 C. In these locations, a low temperature susceptiblebinder is considered to be sufficient provided the binder

exhibits the required performance at high temperatures.

Pavem

ent t

empe

ratu

re,

C

Time, hr

Fig. 4 Comparison of pavement temperatures measured by HRS [5]and predicted by regression equation


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Conclusion

The choice of a correct quality of the binder in a pavement

construction is paramount in ensuring that it exhibits the

required performance for the complete design life. This

paper focussed on the providing the information related to

expected temperature ranges during the design life. ANN

was used to predict air temperature for the design period

using 30 years of history data for 37 locations across India.

The design air temperatures were estimated and converted

to pavement temperature using regression model developed

as a part of this study. From the pavement temperatures

calculated, pavement temperature (low and high) contours

were drawn for the first time for India.

From the pavement temperature contours, it was seen

that the design maximum pavement temperature varied

Table 2 Design temperatures for selected locations

Loc ID Location Maximum air

temperature, CMinimum air

temperature, CDesign pavement temperature for

75 % reliability (C)Design pavement temperature for

99 % reliability (C)

Mean SD Mean SD Max Min Max Min

1 Anantpur 33.9 3.24 22.66 2.65 56.31 17.77 63.31 14.60

2 Aurangabad 32.13 3.4 18.39 4.12 54.71 12.26 62.06 7.34

3 Bangalore 29.2 2.5 19.07 1.74 49.34 16.32 54.75 14.24

4 Belgaum 30.14 3.41 18.24 2.36 51.68 14.76 59.05 11.94

5 Bhopal 31.35 4.48 18.45 5.14 55.04 10.40 64.72 4.26

6 Bhubaneshwar 32.53 2.53 21.96 3.8 54.54 15.13 60.01 10.59

7 Chennai 32.52 2.54 24.53 1.95 53.75 20.00 59.24 17.67

8 Coimbatore 32.21 2.17 21.37 1.36 52.80 18.47 57.49 16.85

9 Cudappah 34.8 3.55 22.85 3.09 57.76 17.40 65.43 13.71

10 Delhi 30.85 5.72 18.73 7.21 56.04 6.80 68.41 -1.81

11 Dharamshala 23.83 4.88 14.51 5.72 46.58 4.82 57.13 -2.02

12 Dibrugarh 27.67 2.18 18.7 5.15 48.69 9.73 53.40 3.58

13 Gorakhpur 31.19 4.64 19.18 6.1 55.33 9.02 65.36 1.73

14 Guwahati 29.11 2.89 19.62 5.13 51.06 10.67 57.30 4.54

15 Gwalior 31.9 5.11 18.05 6.9 56.63 7.21 67.68 -1.03

16 Hyderabad 32.1 3.61 20.76 3.22 54.60 15.40 62.40 11.55

17 Imphal 26.7 2.66 15.42 5.83 47.53 7.08 53.28 0.11

18 Jagdalpur 31.24 3.4 19.13 4.34 53.50 12.60 60.85 7.41

19 Jaipur 31.41 5.46 18.91 6.25 56.34 8.61 68.14 1.14

20 Jharsuguda 32.7 3.98 21.01 4.73 56.18 13.06 64.78 7.41

21 Jodhpur 33.29 4.63 19.25 6.36 58.02 8.79 68.03 1.19

22 Kakinada 32.52 2.35 24.24 2.14 54.01 19.13 59.09 16.57

23 Kanyakumari 30.6 0.55 24.34 0.69 49.00 21.34 50.19 20.52

24 Kolkatta 31.29 2.73 22.07 4.51 53.34 13.96 59.24 8.57

25 Kottayam 31.77 1.36 22.3 0.56 51.34 19.89 54.28 19.22

26 Kurnool 34.55 3.63 22.97 2.9 57.65 17.54 65.50 14.07

27 Lucknow 31.58 5.17 18.59 6.65 56.31 7.85 67.49 -0.10

28 Mumbai 31.86 1.23 22.66 3.24 52.36 16.55 55.01 12.68

29 Nagpur 33.29 4.43 20.7 4.75 57.29 12.88 66.87 7.21

30 Patiala 29.32 5.75 17.64 7.05 54.28 5.80 66.71 -2.62

31 Patna 31.16 4.54 19.61 6.15 55.08 9.49 64.90 2.14

32 Raipur 32.97 4.09 20.5 4.79 56.63 12.61 65.47 6.89

33 Rajkot 33.82 3.52 20.88 4.34 57.31 13.34 64.92 8.15

34 Ramagundam 34 3.81 21.84 4.43 57.38 14.52 65.62 9.23

35 Satna 32.04 4.86 18.93 6.51 56.40 8.73 66.90 0.95

36 Srinagar 19.77 5.27 7.83 7.11 41.85 -2.34 53.24 -10.84

37 Trichy 33.91 2.77 23.88 2.09 55.48 19.63 61.47 17.13


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from 54 to 68 C with highest temperature in the centralpart of India. The design minimum pavement temperatures

went sub-zero in some locations in the northern part of

India while up to 20 C was observed in the southern mostpart of India. On similar lines, detailed analysis of traffic is

also necessary before one could make a judicious choice on

the type of binder required to be used for any location. The

regression equation developed in this study has to be val-

idated with more data points as data becomes available.

The binder properties have to be ascertained specific to the

location it is serving and they have to be correlated to the

field performance. All these require enormous data col-

lection and this is the need of the hour for India.

Acknowledgments The authors are thankful to Department ofScience and Technology, Govt. of India for research grant DST/TSG/

STS/2011/46 and Indian Meteorological Department, Pune for pro-

viding weather data.

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Development of Pavement Temperature Contours for IndiaAbstractIntroductionData CollectionAir Temperature ForecastPavement Temperature ModelPavement Temperature Contours

ConclusionAcknowledgmentsReferences

Pavement Temperature Contours

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