Spatial Backfitting of Roller Measurement Values from a Florida Test Bed

TL;DR

This paper analyzes large-scale roller measurement data from a Florida test bed, using spatial backfitting to understand the impact of roller driving direction on compaction measurements.

Contribution

It introduces a sequential spatial backfitting algorithm to analyze complex, multivariate measurement data collected by earthwork rollers, highlighting the importance of driving direction.

Findings

Driving direction significantly affects measurement values.

Empirical semivariogram analysis reveals complex spatial correlation structures.

Spatial backfitting improves understanding of measurement variability.

Abstract

Modern earthwork compaction rollers collect location and compaction information as they traverse a compaction site. These data are indirectly observed through non-linear measurement operators, inherently multivariate with complex correlation structures, and collected in huge quantities. The nature of such data was investigated at a large, atypically compacted test bed in Florida, USA. Exploratory analysis of this data through detrending and empirical semivariogram estimation is performed. A second analysis using a sequential, spatial backfitting algorithm is used to investigate the importance of driving direction of the roller.