Prediction of the California bearing ratio from some field measurements of soils

Author:

Gül Yavuz1ORCID,Çayir Haci Mehmet2ORCID

Affiliation:

1. Assistant Professor, Department of Mining Engineering, Faculty of Engineering, Sivas Cumhuriyet University, Turkey (corresponding author: )

2. Geological Engineer, Argesel Engineering Company Limited, Sivas, Turkey

Abstract

In this study, the fast and safe predictability of the California bearing ratio (CBR) from some important soil parameters that can be obtained easily and cheaply was investigated. Within the scope of this study, the CBR values of 21 different soils in different regions of Sivas province (Turkey) were determined in situ by CBR tests. Then, standard penetration tests (SPT) and ground vibration measurements were conducted on the same soils. At the same time, some physical properties of the soils were determined as a result of field and laboratory studies. Regression analyses were carried out in order to develop correlations between the CBR, and field and laboratory study results. Significant and highly correlated relations between the CBR and peak particle velocity, blow number (SPT-N value), water content (w) and dry unit weight(γd), were obtained. Prediction performance of all proposed equations were tested by using the root mean square error (RMSE) and variance account for (VAF) indices, and particularly, correlations obtained between the CBR – ground vibration and CBR – standard penetration values were observed to have high prediction performance. Good performance indices (correlation coefficients, RMSE, VAF) indicate that the CBR values of soils can be used for initial assessment.

Publisher

Thomas Telford Ltd.

Subject

Civil and Structural Engineering

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Prediction of CBR by Deep Artificial Neural Networks with Hyperparameter Optimization by Simulated Annealing;Indian Geotechnical Journal;2024-02-04

2. A Scientometrics Review of Soil Properties Prediction Using Soft Computing Approaches;Archives of Computational Methods in Engineering;2023-11-24

3. CBR Prediction of Pavement Materials in Unsoaked Condition Using LSSVM, LSTM-RNN, and ANN Approaches;International Journal of Pavement Research and Technology;2023-02-13

4. Prediction of soaked CBR of fine-grained soils using soft computing techniques;Multiscale and Multidisciplinary Modeling, Experiments and Design;2022-10-08

5. Meta-Analysis of Soil Property Relationships and Construction Platform Design;Transportation Research Record: Journal of the Transportation Research Board;2022-09-27

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