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Resilient Modulus Behavior Estimated from Aggregate Source Properties
Resilient modulus (MR) is a key mechanistic pavement analysis input for designing conventional flexible pavements with unbound aggregate base and granular subbase layers. For satisfactory pavement design and performance, it is often challenging to determine unbound aggregate layer modulus inputs when only limited aggregate source property data are available. This paper presents established correlations between aggregate physical properties and stress-dependent MR characterization model parameters by utilizing the Minnesota Department of Transportation (Mn/DOT) aggregate property databases. In addition to gradation, percent passing No. 200 sieve (or fines content), moisture content and dry density, aggregate particle shape properties quantified as Flat and Elongated (F&E) ratio, Angularity Index (AI) and Surface Texture (ST) index by the University of Illinois Aggregate Image Analyzer (UIAIA) were also included as predictor variables for developing correlations. A subsequent Monte Carlo type simulation was performed via the software @RISK to investigate sensitivities of MR to the various aggregate source properties. It was found that the inclusion of aggregate shape properties significantly improved the established correlations. On the basis of Monte Carlo simulation results, the design reliability of the current MnPAVE program Fall input moduli for aggregate base/granular subbase materials was demonstrated to be greater than the current estimate of 85%.
Resilient Modulus Behavior Estimated from Aggregate Source Properties
Resilient modulus (MR) is a key mechanistic pavement analysis input for designing conventional flexible pavements with unbound aggregate base and granular subbase layers. For satisfactory pavement design and performance, it is often challenging to determine unbound aggregate layer modulus inputs when only limited aggregate source property data are available. This paper presents established correlations between aggregate physical properties and stress-dependent MR characterization model parameters by utilizing the Minnesota Department of Transportation (Mn/DOT) aggregate property databases. In addition to gradation, percent passing No. 200 sieve (or fines content), moisture content and dry density, aggregate particle shape properties quantified as Flat and Elongated (F&E) ratio, Angularity Index (AI) and Surface Texture (ST) index by the University of Illinois Aggregate Image Analyzer (UIAIA) were also included as predictor variables for developing correlations. A subsequent Monte Carlo type simulation was performed via the software @RISK to investigate sensitivities of MR to the various aggregate source properties. It was found that the inclusion of aggregate shape properties significantly improved the established correlations. On the basis of Monte Carlo simulation results, the design reliability of the current MnPAVE program Fall input moduli for aggregate base/granular subbase materials was demonstrated to be greater than the current estimate of 85%.
Resilient Modulus Behavior Estimated from Aggregate Source Properties
Xiao, Yuanjie (author) / Tutumluer, Erol (author) / Siekmeier, John (author)
Geo-Frontiers Congress 2011 ; 2011 ; Dallas, Texas, United States
Geo-Frontiers 2011 ; 4843-4852
2011-03-11
Conference paper
Electronic Resource
English
Resilient Modulus Behavior Estimated from Aggregate Source Properties
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