Application of a hybrid neural network structure for FWD backcalculation based on LTPP database-Reference-Cited by-同舟云学术

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

Published:2021-03-09 Issue: Volume: Page:1-14
ISSN:1029-8436
Container-title:International Journal of Pavement Engineering
language:en
Short-container-title:International Journal of Pavement Engineering

Author:

Han Chengjia¹,Ma Tao¹²,Chen Siyu¹^ORCID,Fan Jianwei¹

Affiliation:

1. School of Transportation, Southeast University, Nanjing, People’s Republic of China

2. National Engineering Laboratory of Highway Maintenance Technology, Changsha University of Science & Technology, Changsha, People’s Republic of China

Funder

National Natural Science Foundation of China

Project, Department of Transportation of Shandong Province

Jiangsu Highway Engineering Maintenance Technology Co., Ltd

Huai'an Highway Administration Office

Bureau of Transportation Construction of Jiangsu

Publisher

Informa UK Limited

Subject

Mechanics of Materials,Civil and Structural Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/10298436.2021.1883016

Reference34 articles.

1. Simplified Closed-Form Procedure for Network-Level Determination of Pavement Layer Moduli from Falling Weight Deflectometer Data

2. Big data analysis for brain tumor detection: Deep convolutional neural networks

3. Backcalculation of full-depth asphalt pavement layer moduli considering nonlinear stress-dependent subgrade behavior

4. Wide & Deep Learning for Recommender Systems

5. Cho, K. et al., 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078.

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