Implementation framework of the UIUC aggregate base rutting model

ABSTRACT

This paper presents findings from a research study focused on calibrating and improving the robustness of a permanent deformation model for unbound aggregate base/subbase materials, recently developed at the University of Illinois and referred to as the UIUC rutting model. The model provides estimates for permanent strains as a function of load cycles, applied deviator stress, and the ratio of mobilized shear stress to strength. Sixteen aggregate materials were tested for permanent deformation both at their source gradations, and an engineered gradation. Based on multiple linear regression analyses, the UIUC rutting model parameters were determined to estimate the laboratory-measured permanent strains with high accuracy. Constrained and stepwise regression statistical approaches were employed to reduce variability in the regression model parameters and express the model parameters as function of material properties such as shear strength, morphological shape properties, gradation, and compaction characteristics. Results from stepwise analyses indicated that the most accurate predictions could be achieved by considering particle shape properties. Finally, a framework was established to predict unbound aggregate permanent deformation trends of flexible pavement base/subbase layers using the UIUC model parameter equations obtained from stepwise regression, along with the physical/mechanical properties of the aggregate materials determined from standard laboratory tests.

KEYWORDS:

• Aggregates
• permanent deformation
• triaxial testing
• shear strength
• UIUC rutting model
• constrained regression
• stepwise regression

Acknowledgement

This research was conducted at the Illinois Center for Transportation (ICT) of the University of Illinois at Urbana-Champaign (UIUC). The authors would like to thank Judith Corley-Lay (now retired) and Clark Morrison with North Carolina DOT (NCDOT) for their support and acknowledge the help of NC aggregate producers for providing the materials used in the laboratory study. Many thanks to Liang Chern Chow for conducting the laboratory tests on materials at the Engineered Gradation, and for ICT graduate student Osman Erman Gungor for his help in developing the MATLAB code used in conducting the constrained regression analyses. The contents of this paper reflect the views of the authors who are responsible for the facts and the accuracy of the data presented. This paper does not constitute a standard, specification, or regulation.

Disclosure statement

No potential conflict of interest was reported by the authors.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This work was supported by North Carolina Department of Transportation (NCDOT): [Grant Number FHWA/NC/2015-23].