Browsing by Subject "Soil testing"
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Item Suitability of the methylene blue test for surface area, cation exchange capacity and swell potential determination of clayey soils(2008) Yukselen Y.; Kaya A.Application of the methylene blue test methods in determining soil properties, including specific surface area (SSA), cation exchange capacity (CEC), swell index, and swell potential are investigated on clayey soil samples with widely different mineralogy. The results indicate that the MB methods yield accurate prediction of some soil index properties, and they are easy to apply with simple test equipment. The results also show that the testing methods can be applied for soils that have widely different mineralogy. External and internal surface areas of soils can be measured by the MB adsorption methods. Effect of particle size on the MB surface area measurement accuracy was also studied using samples passing 0.425 mm (No. 40) and 0.075 mm (No. 200) sieves. The results show that there is no significant difference in the amount of absorbed methylene blue of the soil samples passing the No. 40 and No. 200 sieves. The test results also indicate that the MB-CEC values are generally lower than those obtained by the ammonium acetate method. The correlation coefficient between the MB-CEC and NH4-Na results is 0.88 indicating that MB can be used effectively to measure CEC of soils. The results also show that swell index and swell potential of the soils can be estimated with MB methods accurately, economically and readily. Significant relationship is observed between the swelling potential and MBV (methylene blue value) for a wide range of soils. A new classification for swelling soils is proposed using MBV. © 2008 Elsevier B.V.Item Method dependency of relationships between specific surface area and soil physicochemical properties(2010) Yukselen-Aksoy Y.; Kaya A.It is postulated that the behavior of fine-grained soils may be explained by the relationship between surface area and other geotechnical properties. To this end, there are several studies correlating geotechnical indexes with specific surface area (SSA). However, there is no universally accepted specific surface area determining method as several methods are available. Depending on the method employed, the measured specific surface area may show variations for a given soil. This is because the predictive power of each method depends on the type of minerals and organic matter that are present in the soil. Thus, different SSA determination methods yield widely different estimates of index properties and regression equations. To examine the role of method on SSA of soils, the SSAs of 32 soils with different mineralogies were determined using BET-N2, EGME, MB-titration, and MB-spot test methods. The measured SSA of soils was correlated with their respective geotechnical index properties. Further, the data obtained in this study and those reported by previous researchers were compared. The results suggest that correlations between geotechnical index properties and SSA using different methods may not be comparable. Accurate prediction, however, is provided only if the relationship is calibrated using soils having similar physical and chemical characters. © 2010 Elsevier B.V.Item Predicting soil swelling behaviour from specific surface area(2010) Yukselen-Aksoy Y.; Kaya A.Some geotechnical index properties, such as the liquid limit, plasticity index, clay content and cation exchange capacity, have been used to predict the swelling potential of soils. However, a literature review indicates that prediction of the swelling potential of soils using these index properties is not completely successful. At the same time, the methods used to determine swelling potential are time-consuming. Thus researchers have been investigating other methods that can predict the swelling potential of soils readily and accurately. To this end, in this study the BET (Brunauer, Emmett and Teller equation)-N2 adsorption, ethylene glycol monoethyl ether (EGME) and methylene blue (MB) measured specific surface areas (SSA) are correlated with the swell index and modified free swell index of soils. The SSA and swell index of 16 remoulded and 15 undisturbed soils consisting of a wide range of mineralogy were determined. Results indicate that the correlation between the SSA and the swelling behaviour of the clayey soils examined is significant. A linear relationship is observed between the swell index, Cs, and the MB SSA: the swell index of the soils increases as the SSA increases. The correlation coefficient between the SSA and the modified free swell index (MFSI) is 0.93, indicating that the MB SSA does exert a significant influence on the swelling behaviour of clayey soils. Based on the test results obtained, a new swelling potential classification is proposed.Item The prediction of swell percent and swell pressure by using neural networks(Association for Scientific Research, 2011) Erzin Y.; Güneş N.Expansive soils exhibit significantly high volumetric deformations and so pose a serious threat to stability of the structures and foundations. Thus, determination of their swelling properties (i.e. swelling potential and swell pressure) becomes essential. However, measurement of the swelling properties is time-consuming and requires special and expensive equipment. With this in view, efforts were made to develop artificial neural network (ANN) and multiple regression analysis (MRA) models that can be employed for estimating swell percent and swell pressure. To achieve this, the results of free swell tests performed on statically compacted specimens of Kaolinite-Bentonite clay mixtures with varying soil properties were used. Two different ANN (ANN-1 and ANN-2) and MRA (MRA-1 and MRA-2) models have been developed: ANN-1 and MRA-1 models for predicting swell percent and ANN-2 and MRA-2 models for predicting swell pressure. The results obtained from ANN and MRA models were compared vis-à-vis those obtained from the experiments. The values predicted from the ANN models match the experimental values much better than those obtained from MRA models. Moreover, several performance indices such as determination coefficient (R2), variance account for (VAF), mean absolute error (MAE), and root mean square error (RMSE) were calculated to check the prediction capacity of the ANN and MRA models developed. The obtained indices make it clear that the constructed ANN models have shown higher prediction performance than MRA models. It has been demonstrated that the ANN models can be used satisfactorily to predict swell percent and swell pressure as a rapid inexpensive substitute for laboratory techniques. Copyright © Association for Scientific Research.Item The unique relationship between swell percent and swell pressure of compacted clays(Springer Verlag, 2013) Erzin Y.; Gunes N.Expansive soils exhibit high volumetric deformations, posing a serious threat to the stability of structures and foundations. However, measurement of swelling properties is time consuming and requires special and expensive equipment. This study made an attempt to investigate the relationship between these parameters and easily obtained soil properties using various clay mineral mixtures to obtain soils in a wide range of plasticity indices. Free swell percent was correlated to clay percent, water content, dry unit weight, plasticity index, liquidity index and cation exchange capacity using multiple regression analyses. A very high (R = 0. 94) fit was also found for a proposed relationship between the percent swell and swell pressure values for samples having a swell pressure ≤300 kPa. It is concluded that the proposed equations offer a rapid and inexpensive substitute for laboratory testing of swell percent/swell pressure in the preliminary stages of site investigations. © 2013 Springer-Verlag Berlin Heidelberg.Item Developing cation exchange capacity and soil index properties relationships using a neuro-fuzzy approach(Springer Verlag, 2014) Pulat H.F.; Tayfur G.; Yukselen-Aksoy Y.Artificial intelligence methods are employed to predict cation exchange capacity (CEC) from five different soil index properties, namely specific surface area (SSA), liquid limit, plasticity index, activity (ACT), and clay fraction (CF). Artificial neural networks (ANNs) analyses were first employed to determine the most related index parameters with cation exchange capacity. For this purpose, 40 datasets were employed to train the network and 10 datasets were used to test it. The ANN analyses were conducted with 15 different input vector combinations using same datasets. As a result of this investigation, the ANN analyses revealed that SSA and ACT are the most effective parameters on the CEC. Next, based upon these most effective input parameters, the fuzzy logic (FL) model was developed for the CEC. In the developed FL model, triangular membership functions were employed for both the input (SSA and ACT) variables and the output variable (CEC). A total of nine Mamdani fuzzy rules were deduced from the datasets, used for the training of the ANN model. Minimization (min) inferencing, maximum (max) composition, and centroid defuzzification methods are employed for the constructed FL model. The developed FL model was then tested against the remaining datasets, which were also used for testing the ANN model. The prediction results are satisfactory with a determination coefficient, R2 = 0.94 and mean absolute error, (MAE) = 7.1. © 2014, Springer-Verlag Berlin Heidelberg.Item Analyzing the effect of various soil properties on the estimation of soil specific surface area by different methods(Elsevier Ltd, 2015) Bayat H.; Ebrahimi E.; Ersahin S.; Hepper E.N.; Singh D.N.; Amer A.M.M.; Yukselen-Aksoy Y.Depending on the method used, measuring the specific surface area (SSA) can be expensive and time consuming and limited numbers of studies have been conducted to predict SSA from soil properties. In this study, 127 soil sample data were gathered from the available literature. The data set included SSA values and some of the soil physical and chemical index properties. At the first step, linear regression, non-linear regression, regression trees, artificial neural networks, and a multi-objective group method of data handling were used to develop seven pedotransfer functions (PTFs) for the purpose of finding the best method in predicting SSA. Results showed that the artificial neural networks performed better than the other methods used in the development and validation of PTFs. At the second step, to find the best set of SSA for predicting input variables and to investigate the importance of the input parameters, the artificial neural networks were further used and 25 models were developed. The results showed that the PTF, containing the input variables of sand%, clay%, plastic limit, liquid limit, and free swelling index performed better than the other PTFs. This can be attributed to the close relation between the free swelling index and Atterberg limits with the soil clay mineralogy, which is one of the most important factors controlling SSA. The sensitivity analysis showed that the greatest sensitivity coefficients were found for the cation exchange capacity, clay content, liquid limit, and plasticity index in different models. Overall, the artificial neural networks method was proper to predict SSA from soil variables. © 2015 Elsevier B.V..Item Investigations into factors influencing the CBR values of some Aegean sands(Sharif University of Technology, 2016) Erzin Y.; Türköz D.; Tuskan Y.; Yilmaz I.The California Bearing Ratio (CBR) value of the soils is very important for geotechnical engineering and earth structures. A CBR value is affected by the soil type and different soil properties. With this in view, in this paper, an attempt has been made for investigating the factors that affect the CBR values of some Aegean sands collected from nine different locations in Manisa (Turkey). The sand samples were tested for mineralogy, particle shape and size, and specific gravity. The CBR tests were then performed on these samples at different dry densities to examine the influence 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 coefficient 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. © 2016 Sharif University of Technology. All rights reserved.Item Use of neural networks for the prediction of the CBR value of some Aegean sands(Springer-Verlag London Ltd, 2016) Erzin Y.; Turkoz D.This study deals with the development of an artificial neural network (ANN) and a multiple regression (MR) model that can be employed for estimating the California bearing ratio (CBR) value of some Aegean sands. To achieve this, the results of CBR tests performed on the compacted specimens of nine different Aegean sands with varying soil properties were used in the development of the ANN and MR models. The results of the ANN and MR models were compared with those obtained from the experiments. It is found that the CBR values predicted from the ANN model matched the experimental values much better than the MR model. Moreover, several performance indices, such as coefficient of determination, root-mean-square error, mean absolute error, and variance, were used to evaluate the prediction performance of the ANN and MR models. The ANN model has shown higher prediction performance than the MR model based on the performance indices, which demonstrates the usefulness and efficiency of the ANN model. Thus, the ANN model can be used to predict CBR value of the Aegean sands included in this study as an inexpensive substitute for the laboratory testing, quite easily and efficiently. © 2015, The Natural Computing Applications Forum.Item The use of neural networks for predicting the factor of safety of soil against liquefaction(Sharif University of Technology, 2019) Erzin Y.; Tuskan Y.In this paper, the Factor of Safety (FS) values of soilaga instliquefaction was investigated by means of Artificial Neural Network (ANN) and Multiple Regression (MR). To achieve this, two earthquake parameters, namely earthquake magnitude (Mw) and horizontal peak ground acceleration (a m a x ), and six soil properties, namely Standard Penetration Test Number (SPT-N), saturated unit weight (γsat), natural unit weight (γn), Fines Content (FC), the depth of Ground Water Level (GWL), and the depth of the soil (d), varied in the liquefaction analysis; then, the FS value was calculated by the simplified method for each case by using the Excel program developed and utilized in the simulation of the feed-forward ANN model with backpropagation algorithm and the MR model. The FS values predicted by both ANN and MR models were compared with those calculated by the simplified method. In addition, five different performance indices were used to evaluate the predictabilities of the models developed. These performance indices indicated that the ANN models were superior to the MR model in terms of predicting the FS value of the soil. © 2019 Sharif University of Technology. All rights reserved.