Application of the discrete element method and CT scanning to investigate the compaction characteristics of the soil–rock mixture in the subgrade

Abstract

The soil–rock mixture (SRM) usually contains a large amount of gravels exceeding 40 mm in size, so the traditional laboratory method cannot directly test its maximum dry density (MD), making it difficult to evaluate the compaction degree of the SRM subgrade during construction. In this paper, a numerical simulation method of the vibration compaction method for the SRM (NSM-VCM) was developed based on a discrete element method (DEM) and CT scanning. Based on the established NSM-VCM, the MD of SRMs with a maximum particle size greater than 40 mm (SRM-G) was investigated comprehensively. Based on the results of laboratory tests and the NSM-VCM, a predictive model and determination method of the MD of SRM-G were developed. Finally, field measurements were conducted to validate the laboratory investigations. The results showed that the maximum error between the MD of the SRM obtained from the NSM-VCM and the laboratory test was 0.1%, indicating that the established NSM-VCM has high predictive accuracy. The MD of SRM-G increases with an increasing maximum particle size and dosage of giant granules. Only when the soil–rock ratio is appropriate can SRM-G form a better skeleton dense structure, which is important for improving the MD and mechanical strength. The maximum error between the estimated MD and the measured MD from the field site is 1.3%, which indicates that the prediction model and method for SRM-G established in this paper have high precision. These results address the issue that the MD of SRM-G cannot be determined in a laboratory.

KEYWORDS:

• Soil–rock mixture
• compaction characteristic
• discrete element method
• CT scan
• predictive model

Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under project no. 51908460 and by the Scientific Research of Central Colleges of China for Chang'an University under project no. 300102219520.

Conflict of interest statement

There are no conflicts of interest regarding the publication of this paper.

Additional information

Funding

This work was supported in part by the National Natural Science Foundation of China under project no. 51908460 and by the Scientific Research of Central Colleges of China for Chang'an University under project no. 300102219520.