In this part of the study, two different type of concrete, C30/37 strength class without any glass bead and with glass bead (19 % by weight of total aggregate) were placed on a base course layer (25 cm thick crushed limestone). 19 % proportion of glass bead was in acceptable limit for compressive strength test and ASR in this study. The dimensions of these two slabs were 1 m × 1 m and 25 cm thick. In order to monitor the daily variations of temperature profiles, temperature sensors were installed in the middle of the top and bottom of the slabs. Temperatures were measured between 08:00 and 16:00 h. Because, positive gradient is more important than negative gradient in concrete road design. The test was performed on 03 August 2014 in north side of Turkey. Figure 5 shows the temperature profile of top surface of the standard C30/37 strength class concrete slab and the air.
Figure 5 shows that there is a temperature difference between the top surface of the slab and the air. This difference is 2.6 °C at 08:00 and 17.5 °C at 14:00.
Figure 6 shows the temperature of top and bottom surface of standard C30/37 concrete slab between 08:00 and 16:00. The highest top surface temperature was at 14:00 with a value of 49.7 °C. The biggest temperature difference on top of the slab was 23.3 °C during the testing time. The highest bottom surface temperature was at 16:00 with a value of 36.4 °C. The biggest temperature difference on bottom of the slab was 11.8 °C during the testing time. The biggest temperature difference between top and bottom of the slab was at 14:00 with a value of 14 °C. It is interesting to know that the top and bottom temperatures were same at morning and evening indicating ΔT = 0.
Base on the data given on Fig. 6 two regression model can be developed with high correlation coefficient values:
$$ {\text{Top Surface Temperature }}\left( {\text{C}} \right) = - 0. 7 8 5 \times {\text{Time}}^{ 2} + 10. 4 1 9 \times {\text{Time}} + 1 2. 8 7 8 { }\quad {\text{R}}^{ 2} = 0. 9 1 8 8 $$
$$ {\text{Bottom Surface Temperature }}\left( {\text{C}} \right) = - 0. 3 3 1 5 \times {\text{Time}}^{ 2} + 4. 2 2 8 5 \times {\text{Time}} + 1 8. 2 1 3 { }\quad {\text{R}}^{ 2} = 0. 8 3 2 7 $$
Both polynomial shaped models indicate that the temperatures varied in the sinusoidal form for the time durations studied. Similar trends were observed with concrete samples containing glass beads.
Modelling Temperature Versus Time
Figure 6 shows the temperature of top surface of concrete slab (19 % glass beads in its mixture) between 08:00 and 16:00. The highest top surface temperature was at 14:00 with a value of 37.2 °C. The biggest temperature difference on top of the slab was 15.8 °C during the testing time. The highest bottom surface temperature was at 16:00 with a value of 34.9 °C. The biggest temperature difference on bottom of the slab was 9.8 °C during the testing time. The biggest temperature difference between top and bottom of the slab was at 14:00 with a value of 3.5 °C.
Figure 7 shows the temperature differences of top surface layers of standard C30/37 strength class concrete slab and the concrete slab which has 19 % glass beads in its mix design. The biggest temperature difference was at 14:00 and the top surface temperature of standard concrete slab was 12.5 °C more than the other slab’s top surface temperature at that time.
Figure 8 shows the temperature differences of bottom surface layers of standard C30/37 strength class concrete slab and the concrete slab which has 19 % glass beads in its mix design. The temperature differences were between 0.2 and 3.5 °C. When Figs. 7 and 8 are compared each other, temperature differences of top surface layers are higher than bottom surface layers for both concrete slabs.
Figure 9 shows the bottom surface temperature of December month for C30/37 strength class concretes with 0 and 19 % glass bead in their mix designs. In contrast to the hot season temperature graphs, the graph exponentially decreases to a certain temperature point before midnight and becomes steadily for the measured time duration between midnight and morning. The relationship between time and temperature variation for this study can be given as follows.
$$ {\text{Air Temperature }}(^\circ {\text{C}}) = 9. 80 8 3 {\text{e}}^{{ - 0.0 3 9 {\text{Time}}}} \quad {\text{R}}^{ 2} = 0. 8 1 8 5 $$
$$ {\text{Top Surface Temperature}}(^\circ {\text{C}}) = 1 6. 9 3 8 {\text{e}}^{{ - 0.0 4 {\text{Time}}}} \quad {\text{R}}^{ 2} = 0. 90 9 6 $$
$$ {\text{Bottom Temperature }}(^\circ {\text{C}}) = 2 3. 3 7 7 {\text{e}}^{{ - 0.0 1 9 {\text{Time}}}} \quad {\text{R}}^{ 2} = 0. 6 8 5 5 $$
The correlation coefficient value for air-time model was lower than the bottom and top surface models due to the fact that air temperature varies a lot and the materials can distribute the temperature more homogenous through the surface. Adding glass beads did not change the top surface temperature very much for ow temperatures. The differences between air, top and bottom were almost identical and was about 5–6 °C increment for the same time durations.
The albedo is an important in climatology and computing reflectivity of material surfaces especially in city centers (Li et al. 2012, 2013; Kanok and Farhad 2007). The reflection coefficient or albedo was not measured by a device in this study. However based on visual observation of the radiation reflection an albedo value of 0.5 was estimated. 50 percentage of the light focused on the concrete samples were reflected. The ratio of reflected radiation from the concrete sample surface to the light source upon it was 50 %. At least 50 % was not reflected. Albedo can be expressed as a percentage and is measured on a scale from zero for no reflection such as asphalt surface (0.05–0.15) to 1 for perfect reflection of a white surface. Albedo value can vary between 0.4 and 0.8 for white portland cement concretes. The estimated 0.5 value may increase over the years once the concrete pavement opens to traffic and tires start to wear the surface where later the glass beeds may dominate the surface. The glass beads are used for good night vision on roads especially at nights. Instead of scattering light, glass beads turn the light around and send it back in the direction of the source. The glass bead’s refractive index (RI) is an important physical parameter. The higher the RI of the bead and the fewer impurities in the glass material, the more light is retroreflected. Beads used in this study had RI of 1.5.
Techniques of creating a cool pavement can be classified into two groups based on their mechanism mainly increasing surface reflectance and increasing permeability (Li et al. 2012, 2013). Since research in the area of cool pavements is in an early stage, more techniques will continue to be developed. In this study, different proportions of glass beads, which are used for road marking, were added into the concrete samples to reduce the temperature gradient values representing the temperature changes through the concrete pavement thickness.