Jointed Plain Concrete Pavements (JPCP) are some the most popular forms of concrete pavement that are used in the state of Georgia. Each year the Georgia Department of Transportation (GDOT) inspects and surveys their highways to determine what condition the pavement is in and if any rehabilitation is required to maintain the integrity of the highway. These annual surveys include the JPCP and the key concrete pavement characteristics that are used to determine the condition of the JPCP are the faulting at the joints and the roughness of the section. Since it is well known that concrete will exhibit slight movement when subjected to thermal variations it is possible that the these minor movements could have an impact on the measured slab properties used to rate the JPCP section. The focus of this research is to develop a methodology to use three dimensional technologies to capture JPCP surface data under a variety of thermal conditions, to develop a procedure to collect and analyze concrete temperature data, to develop a method to analyze the surface data and how to correlate all of the data that was collected. Three test sites were chosen that covered a total of 6 test sections that were composed of 25 slabs and 26 joints each. This provided a total of 150 slabs and 156 joints that were used for analysis. A single slab was selected as a test specimen to install thermal logging devices into so that the temperature distributions through the slab could be investigated. Three positions were monitored to determine if the position that the temperature gradient was measured was critical. It was found that the temperature followed a similar trend for all of the positions with the profiles being slightly shifted from each other. It was also concluded that the temperature in the bottom of the slab was approximately the same as the temperature in the base. It was discovered that the maximum positive temperature gradient occurred simultaneously with the maximum ambient air temperature and the maximum surface temperature. The results showed that the surface temperature followed a trend similar to the ambient air temperature. However the surface temperature was greater throughout the day. The faulting analysis results indicated that out of the 156 joints inspected only 15 showed a variation in the average faulting that was greater than the 0.5 mm (0.02 in) accuracy of the sensors used to collect the JPCP surface data. Further investigation revealed that there was no clear trend between the temperature change and the average faulting variation. It was concluded that if there was a change in the average faulting due to temperature it is smaller than what can be depicted by the sensing vehicle and it is less than the 1 mm (0.04 in) measurement accuracy that is specified in the American Association of State Highway and Transportation Officials (AASHTO) R36-04 specification which governs the accuracy requirements for automated faulting measurement methods. The International Roughness Index (IRI) was the method used to measure the roughness on each test site for each data collection run. This resulted in 336 IRI values that were inspected to determine whether there was an impact from the temperature variations. The IRI results showed that the roughness of the test sections did vary through the day. After it was found that the IRI did vary through the day the IRI distributions were compared to the temperature distribution and 7 out of the 12 distributions studied showed a weak correlation between the temperature and the IRI. The amount of variation in the IRI was not quantified because the exact accuracy of the IRI values attained from the sensing vehicle was unknown. However it was attempted to validate the system and determine the accuracy but one of the validation test sections showed disappointing results while the other two showed promising results. Further research is required to fully evaluate the sensing vehicles ability and accuracy when measuring the IRI. A procedure was also developed to extract the longitudinal and transverse curvature of the concrete pavement slabs. Three test slabs were selected at one of the test sites and curvature results were generated using the developed procedure. The curvature results were visually and quantitatively assessed. The visual analysis indicated that the curvature profiles measured by the 3D line lasers did change throughout the data collection, but the patterns did not follow what was expected and a correlation could not be created with the temperature. The quantitative results for the longitudinal curvature revealed that one of the slabs did show a pattern that followed the temperature changes during the data collection, but it did show as much as 4.65 mm (0.183 in) of change between consecutive data collection runs. The longitudinal curvature results for the other two slabs did not show a trend and exhibited unlikely changes in the curvature measured between consecutive data collection runs, which in some instances the deviation was as much as 12.09 mm (0.480 in). For the transverse curvature one of the slabs indicated that the curvature did not change during the data collection, while the other two showed sudden changes as high as 2.16 mm (0.085 in) between consecutive data collection runs. The developed procedure is only preliminary and needs to be further evaluated and refined for it to be able to adequately measure the curvature of as slab. The results also need to be verified using actual measured ground truth curvatures to determine the validity of using the developed procedure and the 3D line laser data to measure the curvature of concrete slabs. Once the procedure is proven to produce reliable results it should be compared to other curvature computation methods, such as those that utilize road profilers or LIDARs, to determine which method is the best.