Investigation of factors affecting the dispersibility of clays and estimation of dispersivity


Turgut A., Isik N. S. , Kasapoglu K. E.

BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT, cilt.76, ss.1051-1073, 2017 (SCI İndekslerine Giren Dergi) identifier identifier

  • Cilt numarası: 76 Konu: 3
  • Basım Tarihi: 2017
  • Doi Numarası: 10.1007/s10064-016-0935-x
  • Dergi Adı: BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
  • Sayfa Sayıları: ss.1051-1073

Özet

Dispersive soils have caused failure of many slopes and earth fills due to external and internal erosion. This study aims to investigate various factors used for identification of dispersivity and to develop some new approaches for the prediction of dispersivity of clays. To achieve this purpose, physical and index properties, as well as degree of dispersivity of 29 clay samples taken from five different locations in and around the city of Ankara were determined. Various statistical prediction models were used for prediction of new dispersivity classes obtained by weighting ranking method. It was determined that dispersivity classes obtained from physical and chemical dispersivity tests performed on the same clay samples using distilled water were different from each other. In addition, crumb and pinhole tests were performed by using test waters with varying TDS values on five selected samples to find the impact of water chemistry on dispersivity. It is concluded from all dispersivity tests that total dissolved salts (TDS) values and sodium percentage (SP) remarkably affect the degree of dispersivity, and the use of these two parameters give more reliable results for the determination of dispersivity. By considering all these facts and to predict the most reliable dispersivity class, all dispersivity classes obtained from physical and chemical dispersivity tests were reevaluated by a weighted ranking system, and new dispersivity classes were assigned. In order to estimate these new dispersivity classes, various statistical models were established by using results of chemical analysis of pore water of clay samples. For this purpose, prediction models including soft computing methods such as decision tree and logistic regression are used and most reliable prediction models having the highest prediction performance are suggested.