Assessment of Geotechnical Properties and Determination of Shear Strength Parameters
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ORIGINAL PAPER
Assessment of Geotechnical Properties and Determination of Shear Strength Parameters Benyamin Ghoreishii . Mohammad Khaleghi Esfahani . Nargess Alizadeh Lushabi . Omid Amini . Iman Aghamolaie . Nik Alif Amri Nik Hashim . Seyed Mehdi Seyed Alizadeh
Received: 9 June 2020 / Accepted: 3 August 2020 Ó Springer Nature Switzerland AG 2020
Abstract In this research, geotechnical properties and the relationship between cohesion (c) and internal friction angle (/) with the SPT-N60 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 / and c based on the SPT-N60. 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 / and results are compared with this research. The obtained results from the ANN and statistical analysis showed that there is a good correlation between / and c derived from the direct shear test with the SPT-N60. Based on R2, RMSE, P-value and Durbin-Watson statistics the correlation between c and the SPT-N60 is stronger than / and the SPT-N60. Moreover, the ANN showed higher accuracy in predicting shear strength parameters compared to the simple regression.
B. Ghoreishii School of Railway Engineering, Iran University of Science and Technology, Tehran, Iran
I. Aghamolaie Ph.D. Department of Geology, Faculty of Science, Shahid Bahonar University, Kerman, Iran
M. Khaleghi Esfahani (&) M.Sc. Graduate of Engineering Geology, University of Isfahan, Isfahan 9177948974, Iran e-mail: [email protected]
N. A. A. N. Hashim Faculty of Hospitality, Tourism and Wellness, University Malaysia Kelantan, Kelantan, Malaysia
N. Alizadeh Lushabi M.Sc. Graduate of Structural Geology, Shahrood University of Technology, Shahrood, Iran
S. M. S. Alizadeh Petroleum Engineering Department, Australian College of Kuwait, West Mishref, Kuwait
O. Amini Faculty of Civil and Surveying Engineering, Graduate University of Advanced Technology, Kerman, Iran
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Geotech Geol Eng
Keywords Artificial neural networks Standard penetration test (SPT) Geotechnical properties Kerman sedimentary basin Shear strength parameters
1 Introduction Estimating geotechnical parameters using in-situ tests such as the standard penetr
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