Utilizing a Neighboring Weighted-Estimation Method for Anomaly Detection with a Continuous Compaction Control Data Set

Continuous compaction control (CCC) systems utilize a mounted accelerometer, GPS unit, and data acquisition system on soil or asphalt compaction equipment, in order to collect data about the compaction process in real time and over 100% of the compaction evaluation area. During their use, CCC systems may collect anomalous data that can have an influence on reported compaction quality results, and which can lead to erroneous conclusions about the degree of compaction of the soil, or inappropriate regression models between CCC data and in situ measurement data. In the current study, an existing neighboring weighted-estimation method was applied to the results from a field study in which a variety of CCC and in situ test data had been collected, to quantify and filter out anomalous data. Other researchers have proposed that this approach can improve presented CCC data, resulting in improved regression relationships between CCC data and in situ measurement data. For the data set that was evaluated, the results indicate that the removal of anomalous data using this methodology did not yield a significant change in the relationships between the CCC and in situ test results.