Engineering properties of soils and the 3D modeling of geological formations are widely used in site investigations and the preparation of geological hazard maps. The present study was conducted to characterize the engineering geological properties of the young surface alluviums of the Gorgan city (Iran) to a depth of 25 m and 3D modeling of their geology using boreholes data. To this end, after determining the location of the available boreholes on the aerial map of Gorgan, four hypothetical cross-sections were considered in the North-South and East-West directions. Then, the borehole data were marked on each section and their 2D geological cross-sections were manually drawn using correlation of the similar layers. In the next step, by expanding the information of these sections, a 3D geological model of Gorgan city was prepared using a conceptual-observational method. According to the evidence from the boreholes and field observations, the depositional environment of Gorgan alluviums was an alluvial fan created by the Ziarat River. Additionally, in terms of engineering characteristics of alluviums, the Gorgan subsurface soils can be divided into four engineering units, including upper clay unit (UCU), middle gravel unit (MGU), lower clay unit (LCU), and sandy unit (SU), which share the same engineering characteristics. Finally, the results of tests performed on samples from different depths were employed to calculate the engineering geological characteristics of each unit, including Atterberg limits, compressibility, undrained shear strength, and drained shear strength parameters. Manuscript profile

A number of common laboratory rock mechanics tests are carried out in all geotechnical projects such as dams, to determine parameters such as porosity, density, water absorption, sonic velocity, Brazilian tensile strength, uniaxial compressive strength, and triaxial compressive strength. In this paper, data obtained from two dams in Asmari Formation including Khersan 1 and Karun 4 - both located in Chahar-Mahal Va Bakhtiari Province, Iran - have been subjected to a series of statistical analyses. Then, using Multivariate Linear Regression (MLR) and Artificial Neural Networks values of UCS, E, C, and φ were predicted using the input parameters including depth, compression ultrasonic velocity, porosity, density, and Brazilian tensile strength. The designed ANN in this research was a feedforward backpropagation network which is powerful tool to solve prediction problems. Designed network had two hidden layer (hidden layer 1: 18 neurons and hidden layer 2: 20 neurons). Via comparing designed MLR and ANN models, it was revealed that ANNs (R2 UCS= 0.91, R2 = 0.87, R2 =0.78, R2 EC phi = 0.61) are more efficient than MLR models (R2 UCS= 0.69, R = 0.69, R = 0.66, and R2 22 EC phi = 0.50) in predicting strength and shear parameters of the intact rock. Also, to enhance the credibility of this study, some extra tests were carried out to evaluate the efficiency of network designed for prediction of strength parameters. The results obtained from this network were as: R2 UCS= 0.85, R2E = 0.81. Manuscript profile