Prediction of California bearing ratio and modified proctor parameters using deep neural networks and multiple linear regression: A case study of granular soils-Reference-Cited by-同舟云学术

Prediction of California bearing ratio and modified proctor parameters using deep neural networks and multiple linear regression: A case study of granular soils

Published:2024-07 Issue: Volume:20 Page:e02800
ISSN:2214-5095
Container-title:Case Studies in Construction Materials
language:en
Short-container-title:Case Studies in Construction Materials

Author:

Polo-Mendoza Rodrigo,Duque Jose,Mašín David

Funder

Center for Geosphere Dynamics, Charles University

Grantová Agentura České Republiky

Publisher

Elsevier BV

Subject

Materials Science (miscellaneous)

Reference137 articles.

1. The effect of overconsolidation on monotonic and cyclic behaviours of frozen subgrade soil;Zhou;Transp. Geotech.,2022

2. Study of the bearing capacity of closely spaced square foundations on granular soils;Fuentes;Geotech. Geol. Eng.,2019

3. Implementation of deep neural networks and statistical methods to predict the resilient modulus of soils;Polo-Mendoza;Int. J. Pavement Eng.,2023

4. Chapter 7: advanced modelling approaches;Mašín,2018

5. Soil behavior and characterization: effect of improvement in CBR characteristics of soil subgrade on design of bituminous pavements;Mukherjee;Indian Geotech. J.,2021

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