AbstractIntelligent compaction (IC) has been successfully used for soil and base compaction of highways. However, the application of IC technology to monitor the construction quality of asphalt pavement faces complications with compaction processing. This study monitored the compaction process of asphalt layers using an IC-based method. The compaction data were first collected during the construction of a local road in Mardan, Pakistan, including IC data, in-place density, and temperature at the asphalt layer surface. The collected IC data were then used to compute the intelligent compaction measurement values (ICMVs). The support vector regression analysis was performed to predict the roller amplitude and in-place density using the ICMVs. To explore the correlations in compaction measuring/monitoring indicators, this study also explored the correlations between the ICMVs with core density, temperature, and amplitude. Experiment results indicated that the predicted roller amplitude values from the support vector regression model were close to the measured ones. There were high correlations between the roller amplitudes and temperatures with the compaction measurement values (CMVs). In contrast, the correlation between the in-place core densities and CMV values was low. Additionally, the CMVs of the backward pass were higher than the forward one in each compaction cycle because the pavement density increased and the air void decreased after each forward pass.

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