Nominal property based predictive models for asphalt mixture complex modulus (dynamic modulus and phase angle)

• Published in: Construction & building materials Vol. 158; pp. 308 - 319
• Main Authors: Nemati, Rasool, Dave, Eshan V.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15-01-2018; Elsevier B.V
• Subjects: Analysis; Asphalt cement; Asphalt mixture; Chemical properties; Dynamic modulus; Generalized regression; Mechanical properties; Phase angle; Prediction models; United States; Dynamic modulus; Asphalt mixture; Phase angle; Prediction models; Generalized regression
• ISSN: 0950-0618; 1879-0526
• DOI: 10.1016/j.conbuildmat.2017.09.144

•Provides models that use only nominal inputs to make reliable property estimates during design phase.•Presents generalized regression framework for developing asphalt property prediction models.•Model is verified through statistical comparisons and comparisons with other predictive models.•Application of proposed model for pavement performance prediction is demonstrated.
Dynamic modulus (|E∗|) and phase angle (δ) are necessary for determining the response of asphalt mixtures to in-service traffic and thermal loadings. While a number of |E∗| and δ predictive models have been developed, many of them require lab measured properties (e.g. binder complex modulus). The majority of previous work has focused only on prediction of |E∗|, limited models exist for prediction of δ. This research utilized generalized regression modelling of lab measurements (from 81 asphalt mixtures) to develop and verify prediction models for |E∗| and δ using only nominal asphalt mix properties that are readily available during the initial mixture design and specification process.