A Novel Multiple Linear Regression Approach for Predicting the Unconfined Compressive Strength of Soil
Publication: International Journal of Geomechanics
Volume 24, Issue 8
Abstract
This paper proposes a precise and general multiple linear regression (MLR) model to predict the unconfined compressive strength (UCS) of various soil types. The study used a wide range of data sets, including 952 data points considering 39 soil types with varying grain sizes. The model inputs were soil physical properties, grain size, age, mixture proportion, and chemical composition of binder materials. An innovative and novel approach was developed to enhance the accuracy of the MLR model, a randomized exploratory algorithm. The model demonstrated significant accuracy with a 0.921 R2 in the testing data set. The Bayesian model averaging (BMA) method was employed for feature reduction, focusing on important variables. Alternative models were also developed based on the significant variables highlighted by the BMA approach, all showing high accuracy in predicting the UCS. The proposed models demonstrated superiority over traditional approaches based on the data set size and statistical metrics. The paper provides instances of predicting soil UCS and determining the mix design corresponding to the target UCS.
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Data Availability Statement
All data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The authors would like to express their gratitude to Mr. Amirhossein Soleimani and Mr. Alireza Mahmoudi for their assistance in this work.
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Published In
International Journal of Geomechanics
Volume 24 • Issue 8 • August 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: May 11, 2023
Accepted: Jan 22, 2024
Published online: May 16, 2024
Published in print: Aug 1, 2024
Discussion open until: Oct 16, 2024
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