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...

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Detalles Bibliográficos
Autores principales: 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
Formato: article
Lenguaje:EN
Publicado: MDPI AG 2021
Materias:
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
Acceso en línea:https://doaj.org/article/59ee155596294df6b4582f9338ca8190
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https://doaj.org/article/59ee155596294df6b4582f9338ca8190

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