In transport sector, improving the quality of road construction plays a crucial role in developing a more sustainable and cost-effective infrastructure network. Intelligent Compaction (IC) has been actively studied as a novel and promising technology for quality monitoring and control in asphalt road construction. However, a successful application of IC in pavement construction is still bothered by its accuracy and stability in quality evaluation. This paper proposed AICV– Acceleration Intelligent Compaction Value, a new evaluation index for harmonic intelligent compaction quality evaluation and with a higher accuracy than the frequently used index CMV– Compaction Measurement Value. In order to optimize the evaluation of the compaction quality uniformity of asphalt pavement, the $3\sigma $ of Normal Distribution is adopted to estimate the compaction uniformity. The statistical analyses of the field measurement data show that, both CMV and AICV are applicable to evaluate the uniformity of compaction quality. However, the dispersion degree of AICV is far less than that of CMV, indicating that AICV is more stable as an index for compaction quality evaluation. By using spatial statistics, the spatial correlation distance of the compaction quality is further obtained, which gives to an assessment of the influence range of intelligent roller. Overall, the study provides a basis for improving the quality control of asphalt pavement construction by means of more delicate monitoring of the compaction process.

Compaction Measurement Value (CMV) is the most widely used quality evaluation index for intelligent compaction. However, with the growth of compaction degree, CMV becomes less accurate due to increased reaction force from densified geomaterials. To solve this problem, this paper proposes a more accurate intelligent compaction quality evaluation index, acceleration intelligent compaction value (AICV). Based on field measurement data analysis, AICV turned out to be effective to elevate quality evaluation accuracy. Additionally, the singularities were detected by $3\sigma$ normal distribution in order to assess the uniformity of subgrade compaction quality. Based on the semivariogram analysis method of spatial statistics, CMV and AICV were analyzed for spatial correlation, and the influence range of intelligent compaction technology employed for subgrade was evaluated. The results from this study provide a basis for further research on precision control and uniformity analysis of intelligent compaction technology.