Improved Prediction of Soil Thermal Properties Using Recurrent Neural Networks-Reference-Cited by-同舟云学术

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Improved Prediction of Soil Thermal Properties Using Recurrent Neural Networks

Published:2024-06-13 Issue: Volume:8 Page:431-441
ISSN:
Container-title:International Conference on Transportation and Development 2024
language:
Short-container-title:

Author:

Slone Scott Michael¹²²²,Zody Zachary¹²²²,Ibey Robert¹²²²,Lein Wade A.¹²²²

Affiliation:

1. Cold Regions Research and Engineering Laboratory, Engineer Research and Development Center, US Army Corps of Engineers, Hanover, NH.

2. Cold Regions Research and Engineering Laboratory, Engineer Research and Development Center, US Army Corps of Engineers, Hanover, NH

Publisher

American Society of Civil Engineers

Link

https://ascelibrary.org/doi/pdf/10.1061/9780784485514.038

Reference11 articles.

1. Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions;Alzubaidi L.;Journal of Big Data,2021

2. Barooni M. Ziarati K. and Barooni A. (2023). Frost Prediction Using Machine Learning Methods in Fars Province. 28th International Computer Conference. Tehran: Computer Society of Iran.

3. Pavement-Transportation Computer Assisted Structural Engineering (PCASE) Implementation of the Modified Berggren (ModBerg) Equation for Computing the Frost Penetration Depth within Pavement Structures

4. Predicting Frost Depth of Soils in South Korea Using Machine Learning Techniques;Choi H.-J.;Sustainability,2022

5. Two-dimensional finite element modeling of long-term frost heave beneath chilled gas pipelines;Dayarathne R.;Cold Regions Science and Technology,2023

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