Relationship Between Index Properties and CBR of Soil and Prediction of CBR

Khatti, Jitendra; Grover, Kamaldeep Singh

doi:10.1007/978-981-19-6774-0_16

Cited by 8 publications

(1 citation statement)

References 10 publications

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“…If PI values increase CBR values decreases (Patel 2010). It was established that CBR and frictional angle can be determined from fine grained soils for varied soil physical properties (Datta 2011, Magdi 2012. 33 soil samples of expansive soil data collected from the on-going road construction site, it was established that Single Linear Regression Analysis (SLRA) and MLRA on CBR and geotechnical parameters It was affirmed that correlation exists among CBR values and soil physical parameters (Valentine, 2017).…”

Section: Introductionmentioning

confidence: 97%

Application of Machine Learning Techniques in Modelling Of Soaked and Unsoaked California Bearing Ratio

Proceedings of the International Conference on Industrial Engineering and Operations Management

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Machine learning techniques such as the Support Vector Machine (SVM), Random Forest (RF), M5 tree, Multiple linear regression and Artificial Neural Network wasadopted to correlate soil physical parameters and California Bearing Ratio (CBR) of soils for Soaked (SCBR) and Unsoaked (USCBR). Four hundred and eighty (480) soil samples were obtained and divided into data set using training and validation of the developed models from some basic soil parameters. Principal Component Analysis (PCA) was implemented to reduce the large dimension of the data set an the actual and predicted values from the models using Root Mean Square Error (RMSE) and coefficient of determination R 2 , it showed

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Section: Introductionmentioning

confidence: 97%