From data in Table 1, graph the penetration (at 25 degreesC) on the original asphalt sample as a function of the flash point (x-axis) in each case. Briefly discuss the trend that emerges.

Air voids in asphalt mix should be not less than :a.6.0 %b.Nonec.5.0 %d.3.0 %

Air voids in asphalt mix should be not less than :a.5.0 %b.3.0 %c.6.0 %d.None

https://docs.google.com/spreadsheets/d/1nh4S5jPhRw7_2KQJuk7-xBu2q0Co1ZsF/edit?usp=drive_link&ouid=113231905501112785105&rtpof=true&sd=true please check this data and give me ideas of the questions i need to ask and answer in this analysis. the charts i need to plot and how best to do my visualization

Table 1: Distance, grades, and rolling resistance on haul road segments.Segment Length (m) Grade (%)RollingResistance (%)Ramp 2500 8 2Ex-pit 500 1 2Dump/Stockpile 500 5 22Figure 1: CAT 795F performance chart in Propulsion mode (uphill)Figure 2: CAT 795F performance chart in retardation mode (downhill)

The properties of asphalt mastic are crucial in determining the moisture sensitivity of asphalt mixtures, but the adhesion performance of the mastic has not been quantitatively described. To address this gap, this study calculated the asphalt mastic surface energy using the equation-of-state method, and established a moisture sensitivity (𝑀𝑆) index based on it. The effectiveness of this index was verified through Marshall testing. First, the surface energy of asphalt and filler was measured, and the critical volume fraction of the mastic was determined using the two-point method. Second, the interaction parameters of asphalt and filler were calculated using the equation-of-state method, and the surface energy of the mastic was obtained. Then the 𝑀𝑆 index was established to evaluate the moisture sensitivity of asphalt mixtures. This new index utilizes the surface energy and aggregate to quantitatively evaluate adhesion, considering the specific surface area of the aggregate and the thickness of asphalt film. Finally, the 𝑀𝑆 was validated through Marshall tests of asphalt mixtures. The results demonstrated that the 𝑀𝑆 obtained by the calculated surface energy exhibits a strong linear correlation with the moisture sensitivity of asphalt mixtures. The goodness of fit reached above 0.90 and the highest was 0.965. This study provided a novel approach to characterize the moisture sensitivity of asphalt mixtures.