Natural Gradient Boosting for Probabilistic Prediction of Soaked CBR Values Using an Explainable Artificial Intelligence Approach

Natural Gradient Boosting for Probabilistic Prediction of Soaked CBR Values Using an Explainable Artificial Intelligence Approach

The California bearing ratio (CBR) value of subgrade is the most used parameter for dimensioning flexible and rigid pavements. The test for determining the CBR value is typically conducted under soaked conditions and is costly, labour-intensive, and time-consuming. Machine learning (ML) techniques h...

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Bibliographic Details
Main Authors: Esteban Díaz, Giovanni Spagnoli
Format: Article
Language:English
Published: MDPI AG 2024-01-01
Series:Buildings
Subjects:
machine learning
CBR
soil index properties
subgrade
compaction characteristics
probabilistic model
Online Access:https://www.mdpi.com/2075-5309/14/2/352

Internet

https://www.mdpi.com/2075-5309/14/2/352

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