Predicting Resilient Modulus of Unbound Granular Base/Subbase Material.

F. Al-Dulaimi, Yasser; M. Awed, Ahmed; Gabr, Alaa R.; EL-Badawy, Sherif M.

doi:10.21608/bfemu.2022.223409

Predicting Resilient Modulus of Unbound Granular Base/Subbase Material.

Document Type : Research Studies

Authors

¹ Alfarabi University, College Civil Engineering Dept.- MSc student, at Public Works Engineering Dept. Faculty of Engineering, Mansoura University, Egypt.

² Assistant Professor, Public Works Engineering Department, Faculty of Engineering, Mansoura University, Egypt.

³ Associate Professor, Public Works Engineering Department, Faculty of Engineering, Mansoura University, Egypt

⁴ Professor, Public Works Dept., Faculty of engineering, Mansoura University, Egypt

10.21608/bfemu.2022.223409

Abstract

This research paper presents the results of modeling the resilient modulus (MR) of unbound granular base/subbase layers by means of the material index properties and stress state. The database employed in this study was collected from literature studies which includes 16 unbound granular materials (nine of them from Virginia, US while the other seven were from different quarries in Egypt). The database includes Liquid limit (LL), plastic limit (PL), plasticity index (PI), weighted PI (WPI), maximum dry density (MDD), optimum moisture content (OMC), passing sieve No. 4 (Pass#4), passing sieve No.200 (Pass#200), and 233 number of MR measurements. Two common literature MR-predictive models were used K-θ and Universal models as the base models. By using the fitting curve toolbox (CFTOOL) in the MATLAB program, the values of the regression coefficients of both models were recalibrated to predict the MR for each material individually. Both models regression coefficients (k-values) were correlated with the index properties of the soils (LL, PL, WPI, MDD, OMC, Pass#4 and Pass#200). Then, the index properties of the investigated UGMs, that affect the MR measurements, were correlated with the recalibrated regression coefficients of both models. Results showed that MR predictions based on index properties and stress state were satisfactory having a coefficient of determination, R² of 0.80, and 0.79 for universal and K-θ models, respectively.

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