CV


Hadi Farhadian

Hadi Farhadian

Associate Professor

عضو هیئت علمی تمام وقت

Faculty: Engineering

Department: Mining Engineering

Degree: Ph.D

CV
Hadi Farhadian

Associate Professor Hadi Farhadian

عضو هیئت علمی تمام وقت
Faculty: Engineering - Department: Mining Engineering Degree: Ph.D |

Dr. Hadi Farhadian is an Associate Professor in the Department of Mining Engineering at the University of Birjand and a researcher in the fields of mineral exploration engineering, hydrogeology, and geomechanics. He obtained his Ph.D. in Mineral Exploration Engineering from Amirkabir University of Technology and has international research experience at the University of Basel and ETH Zurich. Dr. Farhadian’s research interests include numerical modeling, geostatistics, geomechanics, groundwater flow, and data mining. He has published numerous articles in reputable international journals and, in addition to his teaching and research activities, has played an active role in academic administration and innovation.

نمایش بیشتر

Estimation of Groundwater Seepage Risks into Tunnel Using Radial Basis Function Networks

AuthorsHadi Farhadian,Seyed Ahmad Eslaminezhad
Journalعلوم و مهندسی آبیاری
Page number109-124
Serial number45
Volume number2
Paper TypeFull Paper
Published At2022
Journal GradeScientific - research
Journal TypeElectronic
Journal CountryIran, Islamic Republic Of
Journal Indexisc

Abstract

In this study, Site Groundwater Rating (SGR) in the Amirkabir tunnel has been estimated using Radial Basis Function Networks (RBFNs). SGR is the first rating method that by considering the parameters like joint frequency, joint aperture, schistosity, crashed zones, karstification, soil permeability coefficient, tunnel location in the water table or piezometric surface, and the amount and intensity of annual raining in the area, classifies the tunnel path from the risk of groundwater seepage point of view. In this article, using an RBFN, an estimation of SGR along the Amirkabir tunnel path was performed. Field data obtained from primary studies in the tunnel was used to train and test the prepared network. For the testing set, modeling results showed that SGR could be predicted with the mean error of 3.57% and 4.76% using radial basis network and exact radial basis network functions, respectively. A High correlation between the SGR of the tunnel path and the network answers, confirmed the prepared RBFN.

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