Implementing and Optimizing an Artificial Neural Network to Predict Fatigue Resistance of Asphalt Mixtures-Reference-Cited by-同舟云学术

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Implementing and Optimizing an Artificial Neural Network to Predict Fatigue Resistance of Asphalt Mixtures

Published:2023-06-13 Issue: Volume: Page:
ISSN:
Container-title:Airfield and Highway Pavements 2023
language:
Short-container-title:

Author:

Ma Wangyu¹²³⁴⁵,Wang Di¹²³⁴⁵,Chen Chen¹²³⁴⁵,Tang Qinrui¹²³⁴⁵,Gu Fan¹²³⁴⁵

Affiliation:

1. Transtec Group, Mechanicsburg, PA.

2. Dept. of Civil Engineering, Aalto Univ., Espoo, Finland.

3. National Center for Asphalt Technology, Auburn Univ., Auburn, AL.

4. German Aerospace Center (DLR), Institute of Transportation Systems, Berlin, Germany.

5. National Engineering Research Center for Highway Maintenance Technology, School of Traffic and Transportation Engineering, Changsha Univ. of Science and Technology, Changsha, Hunan, China.

Publisher

American Society of Civil Engineers

Link

https://ascelibrary.org/doi/pdf/10.1061/9780784484906.015

Reference14 articles.

1. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

2. Asphalt Institute. (2014). MS-2 asphalt mix design methods, 7th edition.

3. Modelling of 3D response pulse at the bottom of asphalt layer using a novel function and artificial neural network

4. Prediction of geogrid-reinforced flexible pavement performance using artificial neural network approach

5. Application of a hybrid neural network structure for FWD backcalculation based on LTPP database

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