Prediction of pressuremeter modulus and limit pressure of clayey soils by simple and non-linear multiple regression techniques: a case study from Mersin, Turkey


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Kayabaşı A.

ENVIRONMENTAL EARTH SCIENCES, cilt.66, ss.2171-2183, 2012 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 66
  • Basım Tarihi: 2012
  • Doi Numarası: 10.1007/s12665-011-1439-4
  • Dergi Adı: ENVIRONMENTAL EARTH SCIENCES
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.2171-2183
  • Anahtar Kelimeler: Pressuremeter modulus, Limit pressure, Standard penetration test, Mersin (Turkey), Regression, UNIAXIAL COMPRESSIVE STRENGTH, ARTIFICIAL NEURAL-NETWORK, FUZZY MODEL, DEFORMATION MODULUS, ROCK MASSES, PARAMETERS, CPT
  • Eskişehir Osmangazi Üniversitesi Adresli: Evet

Özet

The Standard Penetration Test (SPT) is one of the most frequently applied tests during the geotechnical investigation of soils. Due to its usefulness, the development of empirical equations to predict mechanical and compressibility of soil parameters from the SPT blow count has been an attractive subject for geotechnical engineers and engineering geologists. The purpose of this study is to perform regression analyses between the SPT blow counts and the pressuremeter test parameters obtained from a geotechnical investigation performed in a Mersin (Turkey) city sewerage project. In accordance with this purpose, new empirical equations between pressuremeter modulus (E (M)) and corrected SPT blow counts (N (60)) and between limit pressure (P (L)) and corrected SPT blow counts (N (60)) are developed in the study. When developing the empirical equations, in addition to the SPT blow counts, the role of moisture content and the plasticity index of soils on the pressuremeter parameters are also assessed. A series of simple and nonlinear multiple regression analyses are performed. As a result of the analyses, several empirical equations are developed. It is shown that the empirical equations between N (60) and E (M), and N (60) and P (L) developed in this study are statistically acceptable. An assessment of the prediction performances of some existing empirical equations, depending on the new data, is also performed in the study. However, the prediction equations proposed in this study and the previous studies are developed using a limited number of data. For this reason, a cross-check should be applied before using these empirical equations for design purposes.