Surrogate models to predict maximum dry unit weight, optimum moisture content and California bearing ratio form grain size distribution curve-Reference-Cited by-同舟云学术

Surrogate models to predict maximum dry unit weight, optimum moisture content and California bearing ratio form grain size distribution curve

Published:2021-11-05 Issue: Volume: Page:1-18
ISSN:1468-0629
Container-title:Road Materials and Pavement Design
language:en
Short-container-title:Road Materials and Pavement Design

Author:

Alzabeebee Saif¹^ORCID,Mohamad Safaa A.²^ORCID,Al-Hamd Rwayda Kh. S.³^ORCID

Affiliation:

1. Department of Roads and Transport Engineering, College of Engineering, University of Al-Qadisiyah, Al-Qadisiyah, Iraq

2. Highway and Transportation Engineering Department, College of Engineering, Mustansiriyah University, Baghdad, Iraq

3. School of Applied Sciences, Abertay University, Dundee, UK

Publisher

Informa UK Limited

Subject

Civil and Structural Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/14680629.2021.1995471

Reference72 articles.

1. NCHRP.: Correlation of CBR values with soil index guide for mechanistic and empirical-design for new a rehabilitated pavement structures, Illinois, 2001.

2. An evolutionary modelling approach to predicting stress-strain behaviour of saturated granular soils

3. Prediction of CBR Value of Fine Grained Soils of Bengal Basin by Genetic Expression Programming, Artificial Neural Network and Krigging Method

4. Prediction of California bearing ratio of subbase layer using multiple linear regression models

5. CORRELATION OF CBR VALUE WITH PARTICLES SIZE AND COMPACTION CHARACTERISTICS OF COHESIONLESS SOIL

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