Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Comparing Compressive and Flexural Dynamic Asphalt Modulus by Statistical and Neural Network Modelling
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.
Comparing Compressive and Flexural Dynamic Asphalt Modulus by Statistical and Neural Network Modelling
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.
Comparing Compressive and Flexural Dynamic Asphalt Modulus by Statistical and Neural Network Modelling
RILEM Bookseries
Di Benedetto, Hervé (Herausgeber:in) / Baaj, Hassan (Herausgeber:in) / Chailleux, Emmanuel (Herausgeber:in) / Tebaldi, Gabriele (Herausgeber:in) / Sauzéat, Cédric (Herausgeber:in) / Mangiafico, Salvatore (Herausgeber:in) / Jamshidi, Ali (Autor:in) / White, Greg (Autor:in)
RILEM International Symposium on Bituminous Materials ; 2020 ; Lyon, France
Proceedings of the RILEM International Symposium on Bituminous Materials ; Kapitel: 53 ; 421-427
RILEM Bookseries ; 27
26.09.2021
7 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Asphalt concrete dynamic modulus prediction: Bayesian neural network approach
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