Pouyan PressJournal of Soft Computing in Civil Engineering2588-28722320180701Connectivity and Flowrate Estimation of Discrete Fracture Network Using Artificial Neural Network13265974110.22115/scce.2018.105018.1031ENAkbarEsmailzadehMining and metallurgical department, Urmia university of Technology.0000-0002-8633-9683AbbasKamaliAmirkabir university of technologyKuroshShahriarAmirkabir university of technologyRezaMikaeilUrmia university of Technology0000-0001-8404-3216Journal Article20171108Hydraulic parameters of rock mass are the most effective factors that affect rock mass behavioral and mechanical analysis. Aforementioned parameters include intensity and density of fracture intersections, percolation frequency, conductance parameter and mean outflow flowrate which flowing perpendicular to the hydraulic gradient direction. In order to obtain hydraulic parameters, three-dimensional discrete fracture network generator, 3DFAM, was developed. But unfortunately, hydraulic parameters obtaining process using conventional discrete fracture network calculation is either time consuming and tedious. For this reason, in this paper using Artificial Neural Network, a tool is designed which precisely and accurately estimate hydraulic parameters of discrete fracture network. Performance of designed optimum artificial neural network is evaluated from mean Squared error, errors histogram, and correlation between artificial neural network predicted value and with discrete fracture network conventionally calculated value. Results indicate that there is acceptable value of mean squared error and also major part of estimated values deviation from actual value placed in acceptable error interval of (-1.17, 0.85). In the other hand, excellent correlation of 0.98 exist between predicted and actual value that prove reliability of designed artificial neural network.http://www.jsoftcivil.com/article_59741_bb618b6220c0c38c013d8805ff195a1a.pdf