Analyzing Geotechnical Characteristics of Soils in Erbil via GIS and ANNs

Abstract

The aim of this study is to analyze and model the geotechnical characteristics of soils in Erbil city using Geographic Information Systems (GIS) and Artificial Neural Networks (ANNs). The study used GIS to analyze the geotechnical properties of soils by collecting data from 102 boreholes in three different depth levels (1.5 m–3.5 m, 3.5 m–6.5 m and 6.5 m–9.5 m) to visualize and analyze soil characteristics such as fines content, moisture content, soil plasticity, shear strength parameters, compressibility, Standard penetration test (SPT), and bearing capacity. The paper also establishes the prediction of SPT-N value and bearing capacity based on geotechnical properties of soils using ANN methods and made correlations between SPT values and shear strength parameters with the bearing capacity of the soil. The results analyzed via GIS indicated that the soil classification was silty clay with a small amount of sandy gravel (CL) in most of the study area. According to the SPT–N values, most of the soils in Erbil City ranged between 33 and 50; a higher SPT value generally indicates denser and stronger soil. The value of the shear strength parameter for the maximum friction angle of the soil layers was found to be 36°, and the predominant cohesion was approximately 100 kPa. The compression index of soils ranged between 0.11 to 0.31. The results showed that the ANN models were able to accurately predict the geotechnical parameters of the soil types in the study area. In addition, the use of GIS and ANN techniques allowed for a comprehensive analysis of the geotechnical characteristics of the soils in Erbil, providing valuable information for future construction and development projects.