GEOTECHNICAL CHARACTERIZATION OF ZEOLITESAND AND BENTONITE-SAND MIXTURES GEOTEHNIČNA KARAKTERIZACIJA MEŠANIC ZEOLITA IN PESKA TER BENTONITA IN PESKA


Yildiz Ö., Ceylan Ç.

Acta Geotechnica Slovenica, cilt.19, sa.2, ss.15-32, 2022 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 19 Sayı: 2
  • Basım Tarihi: 2022
  • Doi Numarası: 10.18690/actageotechslov.19.2.15-32.2022
  • Dergi Adı: Acta Geotechnica Slovenica
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.15-32
  • Anahtar Kelimeler: bentonite, correlation, neural networks, prediction, shear strength, zeolite
  • Yıldız Teknik Üniversitesi Adresli: Hayır

Özet

This paper presents the characterization of pure bentonite- and zeolite-type clays and of various contents mixed with sand. The engineering properties of zeolites, bentonites and sand, which are commonly found in Malatya, Turkey, were evaluated in terms of their suitability for geotechnical applications. The crystallinity and structure of solid specimens of bentonite and zeolite were analysed with X-ray diffraction. Then both soils were mixed with sand in various proportions and the enhancement of the engineering properties was investigated. The properties of the mixtures, such as specific gravity, optimum water content, and dry unit weight mixtures, were initially determined. A set of direct shear tests was carried out to determine the shear-strength parameters of the specimens. As a result of extensive laboratory tests, linear correlations were observed between the water content and the consistency limits with the bentonite and zeolite contents in the sand mixtures. The highest for among each sample tested was achieved with the addition of 50 % bentonite and zeolite (i.e., BS50 and ZS50) as 44 and 38 kPa, respectively. A literature survey was carried out to reveal the test results of similar studies. In addition, using the test results from these literature studies and the current study, an NN-based prediction model was developed. The forecast models developed separately for cohesion and internal friction angle had high correlation coefficients: R2 equal to 0.84 for cohesion and R2 equal to 0.78 for the friction angle.