An empirical predictive model for the dynamic resilient modulus based on the static resilient modulus and California bearing ratio of cement- and lime-stabilised subgrade soils

Abstract

This study uses two typical subgrade soil samples selected from a highway construction site in Jilin Province, China, to formulate an empirical predictive model for the dynamic resilient modulus (E_d) based on the static resilient modulus (E_s) and the California bearing ratio (CBR) of cement- and lime-stabilised subgrade soils. Laboratory experiments of the dynamic and static resilient modulus and the CBR are conducted for varying moisture contents, compaction degrees, and number of freeze–thaw cycles. The results indicate that the moisture content, compaction degree, and number of freeze–thaw cycles have similar effects on the dynamic and static resilient modulus, and the CBR of stabilised soils. The results indicate that the E_d, E_s, and the CBR are quadratically correlated with the moisture content, positively correlated with the compaction degree and negatively related to the number of freeze–thaw cycles. The proposed empirical predictive model for the dynamic resilient modulus is finally established based on the static resilient modulus and CBR. It indicates that the logarithmic model could characterise the relationship among the CBR, the E_d, and E_s for cement- and lime-stabilised soils better.

KEYWORDS:

• Cement- and lime-stabilised soil
• moisture content
• compaction degree
• freeze-thaw cycle
• California bearing ratio
• resilient modulus

Acknowledgments

Thank for the financial support by National Natural Science Foundation of China (51878229), Transportation Science and Technology Project of Jilin Province (2017ZDGC-2-2, 2017ZDGC-2-6).

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by National Natural Science Foundation of China: [grant number 51878229]; Transportation Science and Technology Project of Jilin Province: [grant number 2017ZDGC-2-2,2017ZDGC-2-6].