Comparing Compressive and Flexural Dynamic Asphalt Modulus by Statistical and Neural Network Modelling

Ali Jamshidi; Greg White

doi:10.1007/978-3-030-46455-4_53

Back

Comparing Compressive and Flexural Dynamic Asphalt Modulus by Statistical and Neural Network Modelling

Conference paper

Peer reviewed

Comparing Compressive and Flexural Dynamic Asphalt Modulus by Statistical and Neural Network Modelling

Ali Jamshidi and Greg White

Proceedings of the RILEM International Symposium on Bituminous Materials, Vol.27, pp.421-427

RILEM International Symposium on Bituminous Materials, 2020 (Lyon, France, 14-Dec-2020 - 16-Dec-2020)

RILEM Bookseries, 27, Springer International Publishing

2022

DOI: https://doi.org/10.1007/978-3-030-46455-4_53

Files and links (1)

url

https://doi.org/10.1007/978-3-030-46455-4_53View

Published Version

Abstract

Loading frequency

Mix temperature

Response modeling

Structural response

Viscoelastic

Asphalt modulus is the key material characteristic for asphalt layers in layered elastic pavement thickness design. The value of modulus selected by the designer has significant influence on the critical strains calculated in the pavement and consequently the predict life of the structure. Traditionally, a typical modulus value was used for all mixtures and all design scenarios within a given jurisdiction. Importantly, the modulus value used for thickness design is intended to be representative of the asphalt mixture, but because of the impact of vehicle speed and temperature, the actual asphalt modulus changes in the field. This paper compares compressive and flexural modulus values and trends for a range of a typical Australian airport asphalt mixture. In addition, statistical and neural network modelling were used to develop equations for predicting the compressive and flexural moduli of asphalt. The implication of the typical difference in values is estimated and further work is recommended.

Details

Title: Comparing Compressive and Flexural Dynamic Asphalt Modulus by Statistical and Neural Network Modelling
Authors: Ali Jamshidi (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
Greg White (Corresponding Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
Publication details: Proceedings of the RILEM International Symposium on Bituminous Materials, Vol.27, pp.421-427
Conference details: RILEM International Symposium on Bituminous Materials, 2020 (Lyon, France, 14-Dec-2020 - 16-Dec-2020)
Series: RILEM Bookseries; 27
Publisher: Springer International Publishing
DOI: 10.1007/978-3-030-46455-4_53; 10.1007/978-3-030-46455-4
ISSN: 2211-0852
ISBN: 9783030464554
Organisation Unit: University of the Sunshine Coast, Queensland; School of Science, Technology and Engineering
Language: English
Record Identifier: 99578808702621
Output Type: Conference paper

Metrics

26 Record Views