Enhancing intelligent compaction quality assessment utilizing mathematical-geographical data processing

The advent of Intelligent Compaction (IC) has revolutionized real-time monitoring of compaction quality. The Compaction Meter Value (CMV) is widely used in highway construction but demonstrates insufficient reliability, which generates challenges for accurate quality assessment. A mathematical-geographical-based processing method is proposed to refine IC datasets. Six datasets from highway compaction sites were used to verify the effectiveness of the method. Statistical analysis is employed to cleanse redundant values, while a near-neighbor weighted method, accounting for spatial distribution characteristics, is utilized to identify and replace outliers. CMV has instability under complex influence factors, and it shows the best applicability in the subgrade. The optimized datasets perform well in correlation models, showcasing a significant improvement in quality evaluation effectiveness. This paper aims to optimize the utilization of IC datasets, thereby bolstering the reliability of CMV. The proposed method advocates integration into the IC system to promote highway construction quality. •The distribution of IC datasets from subgrade, cement-stable, and asphalt layers are synthetical analyzed.•A mathematical-geographical approach is proposed to identify the effective data for quality evaluations.•The dataset's spatial characteristics are discussed considering construction procedures and geographical principles.•The proposed method is effective in detecting the outliers and enhancing the evaluation accuracy in engineering practice.