Investigations on factors influencing the CBR value of some Aegean sands

Abstract

The California Bearing Ratio (CBR) value of the soils is very important for geotechnical engineering and earth structures. A CBR value is a ected by the soil type and di erent soil properties. With this in view, in this paper, an attempt has been made for investigating the factors that a ect the CBR values of some Aegean sands collected from nine di erent locations in Manisa (Turkey). The sand samples were tested for mineralogy, particle shape and size, and speci c gravity. The CBR tests were then performed on these samples at di erent dry densities to examine the in uence of dry density, relative density, water content, and particle shape and size on the CBR value. Multiple Regression Analysis (MRA) was performed to predict the CBR value of the sands by using the experimental results. Moreover, several performance indices, such as coe cient of correlation and variance account for mean absolute error and root mean square error, were calculated to check the prediction capacity of the proposed MR equation. The obtained indices make it clear that the equation derived from the samples used in this study applies well, with an acceptable accuracy, to the CBR estimation at the preliminary stage of site investigations.

Predicting the Limit Void Ratios of Coarse-Grained Soils Using Artificial Neural Networks

“…Similarly, the emax values predicted by the ANN-2 model were compared with the emax values obtained from literature (Polito, 1999;Santamarina and Cho, 2001;Sukumaran and Ashmawy, 2003;Tika et al, 2003;Erzin, 2004;Park and Byerne, 2004;Cho et al, 2006;Bareither, 2008;Wichtmann and Triantafyllidis, 2013;Belkhatir, 2014;Oh, 2014;Bensoula et al, 2015;Salvatore et al, 2015;Erzin et al 2016) in Figs. 8 and 9, for training and testing samples, respectively.…”

“…The compiled data obtained from the literature (Polito, 1999;Santamarina and Cho, 2001;Sukumaran and Ashmawy, 2003;Tika et al, 2003;Erzin, 2004;Park and Byerne, 2004;Cho et al, 2006;Bareither, 2008;Wichtmann and Triantafyllidis, 2013;Belkhatir, 2014;Oh, 2014;Bensoula et al, 2015;Salvatore et al, 2015;Erzin et al 2016) to form a wider database were used to establish a predictive relationship between the limit void ratios (emax and emin ) of coarse-grained soils and their two graining parameters, namely, mean particle size (D50) and uniformity coefficient (Cu). The descriptive statistics of the data containing the graining parameters and limit void ratios of different 181 soils are given in Table 1.…”

“…ANN-1 and MR-1 for the prediction of the emin values of coarse-grained soils and ANN-2 and MR-2 for the prediction of the emax values of coarse-grained soils. The results predicted from the ANN and MR models were compared with the experimental results taken from the literature (Polito, 1999;Santamarina and Cho, 2001;Sukumaran and Ashmawy, 2003;Tika et al, 2003;Erzin, 2004;Park and Byerne, 2004;Cho et al, 2006;Bareither, 2008;Wichtmann and Triantafyllidis, 2013;Belkhatir, 2014;Oh, 2014;Bensoula et al, 2015;Salvatore et al, 2015;Erzin et al 2016). Moreover, different performance indices, namely, the determination coefficient (R 2 ), the relative root mean square error (RMSE), the mean absolute error (MAE), variance account for (VAF) and, scaled percent error (SPE) were computed for evaluating the prediction performance of the ANN and MR models developed.…”

Research on programmatic multi-attribute decision-making problem: An example of bridge pile foundation project in karst area

“…Assessment and prediction were carried out using the Adaptive Artificial Neural Network (ANN) and MLR method. Erzin [ 74 ] utilized Multiple Regression Analysis to investigate the correlation between nine factors and the California Bearing Ratio (CBR) value of the soils. The factor with the highest correlation coefficient demonstrated a strong relationship.…”

Improvement of the California Bearing Ratio of Peat Soil Using Soybean Crude Urease Calcite Precipitation

“…According to the problem of bearing capacity of peat soil and the results of research that have been carried out in stabilizing, soybean crude urease calcite precipitation (SCU-CP) is used for peat soil improvement as a subgrade to improve California bearing ratio (CBR). The CBR is a critical parameter for evaluating the behavior and performance of a subgrade [17,18]. Furthermore, it is an essential parameter in assessing the serviceability of the subgrade layer [19].…”