Asphalt Binder Performance Grading for the Republic of Yemen Based on Superpave Asphalt Mix-Design

3.1.1. Average 7-days Maximum and Minimum Temperature

In this paper, the average 7-days maximum temperature and minimum temperature in 19 regions of Yemen during 10 years period from 2006 to 2015 were filtered from the collected temperature database. Fig. (1) shows the average 7-days maximum air temperatures during the summertime of 19 cities in Yemen over the past 10 years, the highest temperature was 39.1°C in Al Hudaydah city, and the lowest temperature was 26.39°C in Amran city.

The average minimum air temperature for most of the cities in Yemen in the past 10 years was shown in Fig. (2); only two cities (Dhamar and Sana’a) hit the lowest temperature of -2.5°C, and -1°C, respectively, while the highest of 21.9°C in Hadhramaut. As indicated in Figs. (1 and 2), the results show that it is warm in the cities located along the coastline plus some cities in the highland plateau, and it is cold in the mountain highlands region. The temperature difference between the minimum and maximum is about 13.5°C during summertime and 24.4°C during wintertime. This large seasonal temperature difference in winter indicates that different asphalt binder for low temperature cracking resistance levels used, if the low temperature falls below zero degrees, but if the value ranged between -2.5°C and 21.9°C, for Yemeni region, the predicted min pavement temperature is much higher than the min required for a PG grade, therefore, the low temperature implemented for all PG grade is -10°C.

3.1.2. Trend Analysis of Temperature Variation

Fig. (3) shows the temperature variations of Al Hudaydah and Amran. The two cities reach the average maximum and minimum air temperatures, respectively, in summer. The linear regression analysis of the overall temperature trend showed that the average temperature of Al Hudaydah increased by 0.04°C and Amran decreases by -0.13°C each year. Fig (4) shows the temperature variations in Dhamar and Hadhramaut, in which two cities reach the average maximum and minimum air temperatures, respectively, in winter. According to the results of linear regression analysis, it can be found that the annual average temperature of Dhamar increases by 0.22°C and Hadhramaut decreases 0.06°C.

The linear regression models of average 7-days maximum and minimum air temperature vary, as shown in Figs. (3 and 4), while the linear trends of remaining cities also vary are illustrated in Figs. (5 and 6) to verify the average maximum and minimum air temperature increases or decreases during the past 10 years by the value of -0.03 and 0.07, respectively. It is observed that during the summer, Aden experienced the largest increase in the average 7-days maximum temperature, while in Dhamar, the temperatures tended to drop. In winter, Sana’a and Ibb experienced the largest drop and increment, respectively, in the minimum air temperature, as shown in Figs. (5 and 6).

As shown in Fig. (3) through Fig. (6), Yemenis’ seasonal maximum and minimum air temperatures over the past 10 years have fluctuated unpredictably, rising and falling repeatedly, which makes it difficult to develop a regression analysis formula and then predict the future pavement temperature. Therefore, it is considered to use the mean and standard deviations of air temperature in the past for Yemen to predict air temperature using statistical reliability techniques.

3.2.1. Air-pavement Temperature based on SHARP and LTPP Model

The relationship equations between the highest pavement temperature at 20 mm depth below the pavement surface and the average 7-days maximum air temperature using SHARP Superpave and LTPP system is shown as in Eq. (1) and Eq. (2), respectively. It is found that the relationship between air and pavement temperature varies by the latitude, as presented in Eq. (1).

(1)

(2)

Where

T_20mm = High pavement temperature at a depth of 20 mm,°C

T_air = the highest average air temperature of 7-days,°C

Lat = the weather station’s latitude in degree

T_pav = Hight pavement temperature at depth H from the surface,°C

H = Pavement depth from the surface, mm

While the minimum pavement design temperature on the asphalt pavement surface is equal to the one-day low air temperature because the air temperature is approximately the same as the pavement surface temperature. Eq. (3) and (4) present the model established under the SHARP and LTPP program, respectively.

(3)

(4)

Where

T_min = Minimum pavement temperature,°C

T_air = the low air temperature,°C

Lat = the weather station’s latitude in degree

H = Pavement depth from the surface, mm

3.2.2. Air-pavement Temperature Based on Oman Model

Oman model was firstly developed [16] and further enhanced [19] after gathering more weather data. The final Oman model for the high and the low pavement temperature is expressed, as shown in Eqs. (5) and (6), respectively.

(5)

(6)

Where

T_20mm = High pavement temperature at a depth of 20mm,°C

T_min = Minimum pavement temperature,°C

T_air = the highest average air temperature of 7-days,°C

3.3 Comparison of three prediction models

As illustrated in Fig. (7) through Fig. (10), the Oman relationship model was graphically compared with other models of LTPP and SHARP in terms of the average 7-days maximum and minimum pavement temperatures using the equality lines. The three models were used to compare the maximum and minimum pavement temperatures of 19 cities in Yemen over the past 10 years.

By comparing the three models, the Oman model is in good agreement with the maximum pavement temperature of SHARP and LTPP models, indicating that the pavement temperature obtained by Oman model is lower than that obtained by SHARP and LTPP models. The reason behind the large difference in pavement high temperature could be due to the poor fitting quality of Oman model. However, the maximum pavement temperature obtained using SHARP's equations exhibited slightly higher values compared with that obtained using LTPP's equations, as shown in Figs. (7 and 9). On the other hand, for the minimum temperature, there is no good fit and the graph shows large variations between Oman model and the other two models of SHARP and LTPP, as presented in Figs. (8 and 10). In this paper, the SHARP’s model was adopted to compute the pavement temperatures of the asphalt binder performance level in Yemen, due to the fact that, this model used the dependent variable of latitude that can better reflect the geographical locations of the Yemeni region rather than other models.

4.1.1. Reliability Analysis Approach

In this paper, the use of the maximum and minimum air temperatures attributes to the mean of the average 7-days maximum temperature and the mean of the minimum temperature, respectively, recorded at each weather station. These two means were computed from (n) observations obtained in the (n) years at each station, as presented in Table 4 under the reliability level of 50%. The reliability level of 95% and 98% for the max and min air temperature was calculated based on the standard deviations for the reliability of 50%. In detail, the max temperature at the reliability of 95% is determined as the combination of the mean temperature at the reliability of 50% plus 1.65 multiply by the standard deviation. At the same time, the max temperature at the reliability of 98% is calculated as the combination of the mean temperature at the reliability of 50%, adding 2.055 multiply by the standard deviation. On the other hand, the min temperatures at the reliability of 95% and 98% were calculated in the same way, except that reduction is made from the mean temperature rather than by addition at the reliability of 50%.

The asphalt binder PGs were computed based on the two-reliability level of 50% and 98% considered in this study rather than using the reliability of 95% since the values of 95% and 98% reliability levels were almost the same. PGs with 50% reliability is much chapter than those of 98% reliability, but it is more likely prone to temperature-causing pavement damage. Based on the pavement design method of the Superpave implemented by most states in the US. Through using the cost-benefit analysis, the asphalt binder PGs were determined with the reliability of 98%. However, due to the economic situation in Yemen, PGs with the reliability of 50% might be used in the low-traffic volume road.

4.1.2. Air Temperature Reliability Analysis Results

The analysis results of 50% and 98% reliability at the average of 7-days maximum and minimum air temperatures are shown in Table 4. The asphalt pavement temperatures were determined by converting the average 7-days max and min air temperature using the above mentioned three models (Eqs. 1 to 6) and discussed earlier in the “Prediction of Pavement Temperature” section, then the obtained results presented in Table 5.

4.1.3. Analysis Result of 50% and 98% Reliability

Table 6 shows a comparison of the asphalt binder grades PGs computed by using three models of SHARP, LTTP, and Oman model of average 7-day maximum and minimum pavement temperatures for the specified cities in Yemen at 50%, and 98% reliability.

The results obtained from the SHARP and LTPP model at 50% reliability analysis indicated that the Yemeni region requires three different asphalt binder PGs: PG52-10 (1), PG58-16 (2), and PG 64-10(3), as well as Oman model, requires three different asphalt binder PGs. The 98% reliability analysis results were also similar to those of the 50% reliability analysis, which indicated that the Yemeni region requires three different PGs. However, the pavement temperatures predicted by the SHARP model were relatively higher than those predicted by the other models, especially for maximum pavement temperatures. Moreover, the results showed that the Oman model has a lower pavement temperature than SHARP and LTPP models. The reason for the large difference in high temperatures can be attributed to the low quality of fit of the Oman model. Due to that, the dependent variables of latitude in the Superpave equation can more reflect the geographical locations of Yemeni regions rather than the other models. The SHARP model found out to be more practical than other models from the perspective of the required binder PGs in Yemen. The findings of this study are consistent with those of the other overseas studies. Hassan et al. [ 19 ] and Balushi et al. [ 20 ] tested the SHARP and LTPP models against actual measurements of air and pavement temperatures and concluded that the LTPP model produced closer results to actual temperatures; the authors believe that the LTPP and SHARP models would be more appropriate to be used while the Oman model showed the least quality.

4.1.4. Proposed PG Map

In order to prevent pavement damage due to seasonal temperature variations (which may expose the pavement), the asphalt binder PGs determined through statistical reliability analysis [21].

As shown in Figs. (11 and 12), the asphalt binder PGs map of the Yemeni region was derived from the SHARP model, with a reliability level of 50%, and 98%, respectively. In this study, the most common PGs recommended for low volume roads and heavy traffic volume at 50% and 98% reliability level, respectively, are PG52-10, PG58-16, and PG64-10 (Table 6).