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Honolulu, HI, United States

In order to minimize time and capital investment for preliminary designs of active landslides, researchers have proposed correlation charts relating soil index properties such as LL, PI and CF to residual friction angle. However, estimated residual friction angle shows large variations by chart type and soil properties. This is because the databases that have been used to establish these correlation charts belongs to different soil types and obtained using different testing apparatus with different shearing rates. Therefore, they cannot be generalized. In this study, an artificial neural network (ANN) model was developed to evaluate databases for soils from Britain, China, Japan and the Pacific Islands to determine which soil index properties give best estimate of residual friction angle of these soils. The ANN model developed was further validated using Skempton's (1985) data. The results indicate that there exist a reasonable correlation between soil index properties such as LL, PI, and CF and residual friction, providing that the soils are tested in similar manner and have similar mineralogy. The sensitivity analysis results indicated that residual friction angle is most dependent on CF of soils. The results also show that the ANN model developed is a powerful for predicting residual friction angle of soils using soil index properties. © 2010 Taylor & Francis. Source

Kaya A.,AKAYA and Associates LLC
Computers and Geotechnics | Year: 2010

An artificial neural networks (ANN) model is developed to study the observed pattern of local scour at bridge piers using an FHWA (Federal Highway Administration) data set composed of 380 measurements at 56 bridges in 13 states. Various ANN estimates of observed pier scour depth on different choices of input variables are examined. Reducing the number of variables from 14 to 9 has negligible effect on the coefficient of determination, R2, (0.73 vs. 0.72). Further sensitivity analysis indicates that pier scour depth can be estimated using only four variables: pier shape and skew, flow depth and velocity with a coefficient of determination of 0.81, suggesting that inclusion of some variables actually diminishes the quality of ANN predictions of short term observed pattern of scour. The ANN estimates indicate that flow depth and flow velocity make up 66% of the coefficient of determination. © 2009 Elsevier Ltd. All rights reserved. Source

Yukselen-Aksoy Y.,Celal Bayar University | Kaya A.,AKAYA and Associates LLC
Environmental Earth Sciences | Year: 2011

The purpose of this study was to determine the effects of pH, ion type (salt and metal cations), ionic strength, cation valence, hydrated ionic radius, and solid concentration on the zeta potential of kaolinite and quartz powder in the presence of NaCl, KCl, CaCl2, CuCl2, BaCl2, and AlCl3 solutions. The kaolinite and quartz powder have no isoelectric point (iep) within the entire pH range (3 < pH < 11). In the presence of hydrolysable metal ions, kaolinite and quartz powder have two ieps. As the cationic valence increases, the zeta potential of kaolinite and quartz powder becomes less negative. Monovalent cation, K+, yields more negative zeta potential values than the divalent cation Ba2+. As concentration of solid increases, the zeta potential of the minerals becomes more positive under acidic conditions; however, under alkaline conditions as solid concentration increases the zeta potential becomes more negative. Hydrated ionic radius also affects the zeta potential; the larger the ion, the thicker the layer and the more negative zeta potential for both kaolinite and quartz powder. © 2010 Springer-Verlag. Source

Yukselen-Aksoy Y.,Celal Bayar University | Kaya A.,AKAYA and Associates LLC
Applied Clay Science | Year: 2010

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. Source

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