Multi-faceted investigation and modeling of compaction parameters for road construction

• Published in: Journal of terramechanics Vol. 60; pp. 33 - 42
• Main Authors: Özgan, Ercan, Serin, Sercan, Vural, İsa
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.08.2015
• Subjects: Compaction parameters; Prediction models; Soil compaction; Statistics; Compaction parameters; Prediction models; Statistics; Soil compaction
• ISSN: 0022-4898; 1879-1204
• DOI: 10.1016/j.jterra.2015.02.005

•Correlations coefficients between γd, γn, and w are 0.992, 0.89, and 0.834 respectively.•19.0mm, 12.5mm, 0.180mm and 0.075mm sieves have negative effect on γd.•9.5mm, 4.75mm, 2.0mm and 0.425mm sieves have positive effect on γd.•Prediction models were developed to predict the γd based on the compaction parameters.•Models’ R squares are 0.92, 0.99 and 0.99 for w, γn and including particle diameter. Compaction parameters for road construction were multi-faceted investigated and modeled in this study. For this aim, aggregate samples were randomly taken from D-100/11 highway section in Düzce. Standard Procter tests, sieve analysis, unit weight in natural state (γn), unit weight in dry state (γd), water content (w), physical and mechanical properties of aggregate samples were determined by conducting experiments in laboratory. By using SPSS software programme statistical analyses were performed on the experimental test results. Different prediction models were developed for the prediction of maximum γd based on the compaction parameters and the particle diameter of aggregates. For the maximum γd the correlation coefficient was found perfect level between developed prediction model and the current compaction test results. In order to determine γd, γn and w of compacted aggregate samples in field, different prediction models were also developed based on the penetration test results. As a result, the entire relationship between compaction parameters and particle diameter of aggregates were detailed determined and it was also seen that the developed prediction models can be easy used for the prediction of the compaction test parameters.