Temperature and humidity prediction of mountain highway tunnel entrance road surface based on improved Bi-LSTM neural network-Reference-Cited by-同舟云学术

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Temperature and humidity prediction of mountain highway tunnel entrance road surface based on improved Bi-LSTM neural network

Published:2023-03-27 Issue: Volume: Page:
ISSN:1868-6478
Container-title:Evolving Systems
language:en
Short-container-title:Evolving Systems

Author:

Tao Rui,Peng Rui,Wang Hao,Wang Jie,Qiao Jiangang

Funder

National Natural Science Foundation of China

Consejo Estatal de Ciencia y Tecnología del Estado de Coahuila

Publisher

Springer Science and Business Media LLC

Subject

Control and Optimization,Computer Science Applications,Modeling and Simulation,Control and Systems Engineering

Link

https://link.springer.com/content/pdf/10.1007/s12530-023-09496-y.pdf

Reference24 articles.

1. Alberta CA, Asefzadeh LH, Bayat A (2023) Development of statistical temperature prediction models for a test road in Edmonton. Int J Pavement Res Technol, 10(5):369–382, Alberta, Canada

2. Andersson AK, Chapman L (2011) The impact of climate change on winter road maintenance and traffic accidents in West Midlands, UK. Accid Anal Prev 43(1):284–289

3. Bassan S (2015) Sight distance and horizontal curve aspects in the design of road tunnels vs. highways. Tunn Undergr Space Technol 45:214–226. https://doi.org/10.1016/J.tust.2014.10.004

4. Chen J, Zhao P, Luo Y, Deng X, Liu Q (2017) Damage of shotcrete under freeze-thaw loading. J Civ Eng Manag 23(5):583–593

5. Hao C, Yu G, Xia H (2013) Online modeling with tunable RBF network. IEEE Trans Cybern. https://doi.org/10.1109/TSMCB.2012.2218804

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