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

离群值 压实 数据挖掘 土方工程 计算机科学 土壤压实 土壤水分 环境科学 土壤科学 统计 数学 地质学 岩土工程 人工智能
作者
Yangping Yao,Zhang Xing,Wenjie Cui
出处
期刊:Transportation geotechnics [Elsevier BV]
卷期号:42: 101101-101101 被引量:4
标识
DOI:10.1016/j.trgeo.2023.101101
摘要

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 3σ 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.
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