A LOF-IDW based data cleaning method for quality assessment in intelligent compaction of soils

Intelligent compaction (IC) is attracting increasing interest in construction engineering involving earthwork compaction. However, outliers may exist in data sets collected during IC, which can lead to misinterpretation of the compaction status of soils and may further result in erroneous results for compaction quality assessment. This study proposes a novel method for cleaning the measured data sets by combining the density-based local outlier factor (LOF) and inverse distance weighted (IDW) method. In the proposed LOF-IDW based method, the effect of spatial variation in soil properties is taken into account, while a boxplot-based method is proposed to dynamically determine the suitable threshold for outlier diagnosis. The capability and performance of the proposed method are verified against three data sets collected from construction sites. The results indicate that compared with the commonly used 3 sigma criterion, the proposed method not only exhibits a better performance in outlier diagnosis but also can rehabilitate the relevant data. In addition, the proposed method is demonstrated to be capable of significantly reducing the coefficient of variation of the measured data sets, which provides a more reliable support for the quality assessment and decision-making in IC.

Automated summary

Intelligent compaction (IC) is gaining attention in construction engineering for earthwork compaction. However, outliers in IC data sets can lead to misinterpretation and erroneous quality assessment. This study proposes a method combining density-based local outlier factor (LOF) and inverse distance weighted (IDW) to clean measured data sets. Results show that the proposed LOF-IDW method exhibits better performance in outlier diagnosis, data rehabilitation, and reducing variation, providing reliable support for quality assessment and decision-making in IC.