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Technical Papers
Apr 10, 2017

Correlating Intelligent Compaction Data to In Situ Soil Compaction Quality Measurements

Publication: Journal of Construction Engineering and Management
Volume 143, Issue 8

Abstract

Intelligent compaction (IC) technologies yield a large number of data useful for quality control and quality assurance (QC/QA) in the construction of soil subgrade and embankments. A main challenge of using IC technologies for QC/QA is that the empirical relationships between IC data and in situ measurements are not clear, which prevents transportation agencies from developing an equivalent-criteria specification for the use of IC as a QC/QA tool. This paper describes the results from an experimental research study that was conducted for the Indiana Department of Transportation (INDOT) to assess the correlations between IC data and in situ measurements of compaction quality. Instead of a specifically prepared test site, all the data were collected from a section of U.S. Highway 31 that was under construction. Two types of IC data, compaction meter value (CMV) and machine drive power (MDP), were collected along with the corresponding positions of the rollers. In situ measurements were also conducted to independently assess soil compaction, including dynamic cone penetrometer (DCP), falling weight deflectometer (FWD), and lightweight deflectometer (LWD). Averages of the IC measures were obtained from the local areas of the in situ measurements for correlation analysis. The correlation results provide insights for a state highway agency (SHA) to determine how it may rely on IC for QC/QA in soil compaction. Observed correlations confirm the promise of using IC results to substitute traditional in situ measurements in acceptance tests, which can reduce the requirements for inspection staff to perform in situ tests. The results also suggest that the identification of weak areas, rather than determining acceptance, is the most readily attainable application of IC for QC/QA with soils, particularly with respect to CMV.

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Acknowledgments

The authors gratefully acknowledge the valuable support of INDOT by funding through Joint Transportation Research Program Project SPR-3709 and by the collaboration of the study advisory committee, which comprises personnel from INDOT, the Federal Highway Administration, and Fox Contractors Corporation. The valuable role played by SITECH in data management is also acknowledged and appreciated.

References

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Go to Journal of Construction Engineering and Management
Journal of Construction Engineering and Management
Volume 143Issue 8August 2017

History

Received: Aug 2, 2016
Accepted: Jan 26, 2017
Published online: Apr 10, 2017
Published in print: Aug 1, 2017
Discussion open until: Sep 10, 2017

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Authors

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Hubo Cai, M.ASCE [email protected]
Associate Professor, Lyles School of Civil Engineering, Purdue Univ., 550 Stadium Mall Dr., West Lafayette, IN 47907 (corresponding author). E-mail: [email protected]
Thomas Kuczek [email protected]
Professor, Dept. of Statistics, Purdue Univ., 250 N. University St., West Lafayette, IN 47907. E-mail: [email protected]
Phillip S. Dunston, A.M.ASCE [email protected]
Professor, Lyles School of Civil Engineering, Purdue Univ., 550 Stadium Mall Dr., West Lafayette, IN 47907. E-mail: [email protected]
Ph.D. Candidate, Lyles School of Civil Engineering, Purdue Univ., 550 Stadium Mall Dr., West Lafayette, IN 47907. E-mail: [email protected]

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