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Abstract

This paper investigates representative fine-grained subgrade soils from a highway project. Samples with varying water content and compaction degrees were prepared, and the effects of water content, compaction degree, plasticity index, dynamic deviator stress, and confining pressure on the dynamic resilience modulus (Mr) were analyzed using dynamic triaxial tests. Matlab’s global optimization toolbox was employed to identify the relationships between model parameters ki and physical property indices, thus constructing a prediction model for the dynamic resilience modulus based on physical properties. The predictive performance of this model was compared with conventional models outlined in existing standards. The results show that Mr decreases nonlinearly with increasing dynamic deviator stress and water content, while it increases with rising confining pressure and compaction degree. Mr is inversely proportional to the plasticity index (Ip). Using Matlab’s built-in artificial neural network (ANN) and genetic algorithm (GA) toolkits, a relationship was established between ki and water content (w), compaction degree (K), plasticity index (Ip), and fine particle content (P0.075). This relationship was used to construct a predictive model for the dynamic resilience modulus. Compared to conventional models in standards, the proposed model achieves an average prediction error of 5.03% and a maximum prediction error of less than 15%, demonstrating improved predictive accuracy. Therefore, conducting targeted dynamic resilience modulus tests to develop prediction models based on physical properties holds practical value for specific highway subgrade construction projects.

Publication Date

5-11-2023

DOI

10.14048/j.issn.1671-2579.2023.02.007

First Page

36

Last Page

42

Submission Date

March 2025

Reference

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