On Random Subspace Optimization-Based Hybrid Computing Models Predicting the California Bearing Ratio of Soils

On Random Subspace Optimization-Based Hybrid Computing Models Predicting the California Bearing Ratio of Soils

The California Bearing Ratio (CBR) is an important index for evaluating the bearing capacity of pavement subgrade materials. In this research, random subspace optimization-based hybrid computing models were trained and developed for the prediction of the CBR of soil. Three models were developed, nam...

Full description

Saved in:
Bibliographic Details
Main Authors: Duong Kien Trong, Binh Thai Pham, Fazal E. Jalal, Mudassir Iqbal, Panayiotis C. Roussis, Anna Mamou, Maria Ferentinou, Dung Quang Vu, Nguyen Duc Dam, Quoc Anh Tran, Panagiotis G. Asteris
Format: article
Language:EN
Published: MDPI AG 2021
Subjects:
California Bearing Ratio
modulus of subgrade reaction
elastic modulus
metaheuristic algorithms
Technology
T
Electrical engineering. Electronics. Nuclear engineering
TK1-9971
Engineering (General). Civil engineering (General)
TA1-2040
Microscopy
QH201-278.5
Descriptive and experimental mechanics
QC120-168.85
Online Access:https://doaj.org/article/59ee155596294df6b4582f9338ca8190
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://doaj.org/article/59ee155596294df6b4582f9338ca8190

Similar Items