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Title:

Artificial Neural Network–Based Model to Predict the Complex Modulus and Phase Angle of Asphalt Concrete

Accession Number:

01661585

Record Type:

Component

Abstract:

A new dynamic modulus predictive model based on the artificial neural network methodology is developed in this study for the locally available Superpave asphalt-aggregate mixtures. A total of 54 asphalt-aggregate mixtures were collected, compacted, cored, and sawed to cylindrical test-specimens in the laboratory to conduct dynamic modulus testing. A database containing 1,620 pairs of dynamic moduli and phase angles was then used to develop the predictive model. A neural architecture with 2 twelve-node hidden layers was found to be remarkably suitable for predicting these two properties of Superpave asphalt concrete. Statistical assessment showed that a reasonably accurate estimation of dynamic modulus with coupled phase angle can be achieved by using this predictive model.

Supplemental Notes:

This paper was sponsored by TRB committee AFK50 Standing Committee on Structural Requirements of Asphalt Mixtures. Alternate title: Artificial Neural Network Based Model to Predict the Complex Modulus and Phase Angle of Asphalt Concrete.

Report/Paper Numbers:

18-04869

Language:

English

Authors:

Rahman, A S M Asifur
Tarefder, Rafiqul A

Pagination:

20p

Publication Date:

2018

Conference:

Transportation Research Board 97th Annual Meeting

Location: Washington DC, United States
Date: 2018-1-7 to 2018-1-11
Sponsors: Transportation Research Board

Media Type:

Digital/other

Features:

Figures; References; Tables

Uncontrolled Terms:

Complex modulus; Phase angles

Subject Areas:

Highways; Materials; Pavements

Source Data:

Transportation Research Board Annual Meeting 2018 Paper #18-04869

Files:

TRIS, TRB, ATRI

Created Date:

Jan 8 2018 11:12AM