Superpave Design Aggregate Structure Considering Uncertainty: I. Selection of Trial Blends

摘要

The existing method of selecting Superpave trial aggregate blends isdeterministic and is based on trial-and-error. The primary purpose of this article is todevelop a stochastic optimization model that includes the uncertainties ofindividual aggregate gradations, primary aggregate (PA) properties, and relatedspecifications. The model can directly determine three different trial blends: (1) ablend close to the minimum specification limits, (2) a blend not close to thespecification limits or to the restricted zone (RZ), and (3) a blend close to themaximum specification limits and to the RZ. The constraints of the model includegradation-control specifications, RZ limits, PA properties, and special and unityconstraints. The PA properties include coarse aggregate fractured faces, fineaggregate angularity, sand equivalent, and flat and elongated particles. Theuncertainty is formulated to ensure that the trial blends satisfy model constraints fora specified confidence level. A binary variable is used to allow the designer toproduce a blend that passes below, above, or through the RZ. Application of themodel is illustrated using a numerical example. The proposed model, whichimproves the reliability of trial blends and the efficiency of their selection, should beof interest to practitioners and researchers.