A stochastic neural network based approach for metamodelling of mechanical asphalt concrete properties-Reference-Cited by-同舟云学术

A stochastic neural network based approach for metamodelling of mechanical asphalt concrete properties

Published:2023-03-22 Issue:1 Volume:24 Page:
ISSN:1029-8436
Container-title:International Journal of Pavement Engineering
language:en
Short-container-title:International Journal of Pavement Engineering

Author:

Emig Julius¹,Reuter Uwe²,Bolz Paul Gustav³,Leischner Sabine³,Canon Falla Gustavo³

Affiliation:

1. Fraunhofer Italia Research, Bolzano, Italy

2. Faculty of Civil Engineering, Technische Universität Dresden, Dresden, Germany

3. Institute of Urban and Pavement Engineering, Faculty of Civil Engineering, Technische Universität Dresden, Dresden, Germany

Funder

Deutsche Forschungsgemeinschaft

Publisher

Informa UK Limited

Subject

Mechanics of Materials,Civil and Structural Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/10298436.2023.2177650

Reference32 articles.

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3. A review of surrogate models and their application to groundwater modeling

4. Development of a new revised version of the Witczak E* predictive model for hot mix asphalt mixtures (with discussion);Bari J.;Journal of the Association of Asphalt Paving Technologists,2006

5. Castillo D. 2015. Modeling the impact of air void heterogeneity in the response of asphalt mixtures subjected to mechanical-environmental degradation porcess . Dissertation. Universidad de los Andes Bogota Colombia.

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