Assessment of Geotechnical Properties and Determination of Shear Strength Parameters

• Published in: Geotechnical and geological engineering Vol. 39; no. 1; pp. 461 - 478
• Main Authors: Ghoreishi, Benyamin, Khaleghi Esfahani, Mohammad, Alizadeh Lushabi, Nargess, Amini, Omid, Aghamolaie, Iman, Hashim, Nik Alif Amri Nik, Alizadeh, Seyed Mehdi Seyed
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 2021; Springer Nature B.V
• Subjects: Artificial neural networks; Boreholes; Calcite; Civil Engineering; Clay; Clay minerals; Clinochlore; Correlation; Dolomite; Dolostone; Earth and Environmental Science; Earth dams; Earth Sciences; Geotechnical Engineering & Applied Earth Sciences; Groundwater; Groundwater levels; Hydrogeology; Illite; Illites; Internal friction; Laboratory tests; Liquefaction; Mechanical properties; Minerals; Neural networks; Original Paper; Parameters; Petrography; Petrology; Physical tests; Regression analysis; Sedimentary basins; Shear strength; Shear tests; Soil investigations; Soil mechanics; Soil properties; Soil texture; Statistical analysis; Statistical methods; Statistical tests; Statistics; Terrestrial Pollution; Waste Management/Waste Technology; Shear strength parameters; Geotechnical properties; Standard penetration test (SPT); Kerman sedimentary basin; Artificial neural networks
• ISSN: 0960-3182; 1573-1529
• DOI: 10.1007/s10706-020-01504-1

In this research, geotechnical properties and the relationship between cohesion (c) and internal friction angle (ϕ) with the SPT-N 60 were investigated in 120 boreholes in the sedimentary basin of Kerman. Laboratory tests such as direct shear, triaxial, consolidation, and physical tests were carried out on soil samples extracted from the boreholes, and the SPT test was performed on all 120 boreholes. Since the soil in the area is CL, the SEM, XRD, XRF, physical, and mechanical properties of this soil were investigated. The artificial neural networks (ANN) and statistical analysis were used to estimate ϕ and c based on the SPT-N 60 . The petrography studies revealed that Quartz, Calcite, Dolomite, Albite, Illite, Clinochlore, and Microcline are the most plentiful minerals in this sedimentary basin. Also, the dominant clay is Illite. Illite clays, due to the low shear strength, have made some problems in the earth dams of the studied area. Results show that based on the SPT-N number, groundwater level, and soil texture the liquefaction hazard could not occur in this area. Previous equations are used to predict the c and ϕ and results are compared with this research. The obtained results from the ANN and statistical analysis showed that there is a good correlation between ϕ and c derived from the direct shear test with the SPT-N 60 . Based on R 2 , RMSE, P -value and Durbin-Watson statistics the correlation between c and the SPT-N 60 is stronger than ϕ and the SPT-N 60 . Moreover, the ANN showed higher accuracy in predicting shear strength parameters compared to the simple regression.