Evaluating the use of machine learning for moisture content prediction in base and subgrade layers-Reference-Cited by-同舟云学术

Evaluating the use of machine learning for moisture content prediction in base and subgrade layers

Published:2023-02-27 Issue:12 Volume:24 Page:2910-2928
ISSN:1468-0629
Container-title:Road Materials and Pavement Design
language:en
Short-container-title:Road Materials and Pavement Design

Author:

Huang Yunyan¹,Molavi Nojumi Mohamad¹,Ansari Shadi¹,Hashemian Leila¹,Bayat Alireza¹

Affiliation:

1. Department of Civil and Environmental Engineering, University of Alberta, Edmonton, Canada

Publisher

Informa UK Limited

Subject

Civil and Structural Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/14680629.2023.2182135

Reference38 articles.

1. Predicting unfrozen water contents in frozen soils from surface area measurements;Anderson D. M.;Highway Research Record,1972

2. ARA Inc. ERES Consultants Division. (2000). Guide for mechanistic–empirical design of new and rehabilitated pavement structures. Appendix DD–1: Resilient modulus as function of soil moisture – Summary of predictive models. Final Report. NCHRP Project. A-37A.

3. ARA Inc. ERES Consultants Division. (2004). Guide for mechanistic–empirical design of new and rehabilitated pavement structures. Final Rep. NCHRP Project 1-37A.

4. Introduction to Machine Learning

5. Subgrade Resilient Modulus Correction for Saturation Effects

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