Soft Computing Based Prediction of Unconfined Compressive Strength of Fly Ash Stabilised Organic Clay

Soft Computing Based Prediction of Unconfined Compressive Strength of Fly Ash Stabilised Organic Clay

The current study uses machine learning techniques such as Random Forest Regression (RFR), Artificial Neural Networks (ANN), Support Vector Machines Ploy kernel (SVMP), Support Vector Machines Radial Basis Function Kernel (SVMRBK), and M5P model tree (M5P) to estimate unconfined compressive strength...

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Bibliographic Details
Main Authors: Tammineni Gnananandarao, Rakesh Dutta, Vishwas Khatri, Mummidivarapu Kumar
Format: Article
Language:English
Published: Pouyan Press 2022-10-01
Series:Journal of Soft Computing in Civil Engineering
Subjects:
unconfined compressive strength
organic clay
fly ash
random forest regression
artificial neural networks
svm poly kernel
svmrbk kernel
m5p model tree
Online Access:http://www.jsoftcivil.com/article_158235_ac5ba3299fe4e2c30b6196e191edfe6b.pdf

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