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

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.

Meta-Analysis of Soil Property Relationships and Construction Platform Design

“…Plots of various published models along with the overall best fit model: ( a ) DPI versus UCS ( 5 , 50 , 57 , 71 ), ( b ) CBR versus UCS ( 19 , 72–75 ), ( c ) SPT versus DPI ( 76 , 77 ), ( d ) SPT versus CBR ( 35 , 78 ), ( e ) soaked CBR versus k -value ( 79 ), and ( f ) in-situ CBR versus k -value ( 69 , 80 , 81 ).…”

Artificial Neural Network Models for Predicting California Bearing Ratio of Lateritic Soil Admixed with Reinforce and Rice Husk Ash

“…Based on the results of this study, it is concluded that index soil characteristics and compaction parameters, which have high R 2 values ranging from 0.79 to 0.96 and importance of less than 0.5 for all, can predict CBR of fine-grained soils with exceptional accuracy. The MLRA models discussed in this article are based on fine-grained soils with low plastic content, hence they would not be appropriate for predicting CBR for high plastic content or course-grained soils [14,15].…”

Surrogate models to predict maximum dry unit weight, optimum moisture content and California bearing ratio form grain size distribution curve

“…In addition, these models can also be used to double-check the quality of the laboratory tests, serving as an additional quality control check of the accuracy of the tests conducted in the laboratory. Thus, due to the urgent need for tools to predict these parameters, there have been many attempts in the literature to propose models to aid the prediction using linear regression analysis (LRA) (NCHRP, 2001;Gurtug and Sridharan, 2002;Gurtug et al, 2004;Ali et al, 2019;Katte et al, 2019;Gül and Çayir, 2020), multiple linear regression analysis (MLRA) (Reddy and Pavani, 2006;Vinod and Reena, 2008;Breytenbach et al, 2010;Patel and Desai, 2010;Yildirim and Gunaydin, 2011;Ferede, 2012;Alawi and Rajab, 2013;Mujtaba et al, 2013;Patel and Patel, 2013;Ramasubbarao and Siva Sankar, 2013;Talukdar, 2014;Erzin and Turkoz, 2016a, b;Rehman et al, 2017;Saikia et al, 2017;Al-Hamdani, 2018;Farias et al, 2018;Hohn et al, 2022), and soft computing techniques (Yildirim and Gunaydin, 2011;Venkatasubramanian and Dhinakaran, 2011;Kumar et al, 2013;Erzin and Turkoz, 2016a;Kurnaz and Kaya, 2019;Alam et al, 2020). The information collected from previous studies regarding the type of soil employed in the analysis, number of data points, soil parameters employed in the prediction, technique employed in the prediction, and the proposed models (if applicable) are presented in Table 1.…”