Application of the discrete element method and CT scanning to investigate the compaction characteristics of the soil–rock mixture in the subgrade. Issue 2 (1st February 2022)

• Record Type: Journal Article
• Authors: Ji, Xiaoping; Han, Bo; Hu, Jianming; Li, Shouwei; Xiong, Yue; Sun, Enyong
• Is Part Of: Road materials and pavement design. Volume 23:Issue 2(2022)
• Page Start: 397
• Page End: 413
• Publication Date: 2022-02-01
• Subjects: Soil–rock mixture -- compaction characteristic -- discrete element method -- CT scan -- predictive model; Road materials -- Periodicals; Highway engineering -- Periodicals; Pavements -- Design and construction -- Periodicals; 625.805
• DOI: 10.1080/14680629.2020.1826350
• Languages: English
• ISSNs: 1468-0629

Abstract

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.