Volume 3, Issue 1 (11-2009)
Abstract
(Paper pages 513-522) Estimation of engineering properties of rocks and flow rate is an important issue in rock engineering. Properties of discontinuities have considerable effect on rock mass inflow, because they are the main pass of water flow in fracture rock masses. Despite the bulky research about water flow in rock mass, there is no clear evidence as to relationships between all of these parameters and water inflow in rock masses. Neural network systems have a great advantage in dealing with complicated problems such as forecasting, classification and pattern recognition. In this paper, artificial neural network techniques were used in order to forecast Lugeon amount and Hydraulic conductivity behavior of Granodioritic rock mass of Shoor-Jiroft dam site from some characterization of discontinuities such as Rock quality designation, Fracture frequency, Aperture, Weighted joint density, Fracture zone and depth. Relationships between these factors were analyzed with Simple Linear Regression, Multivariate Regression and Stepwise Regress-ion. A Multilayer Perceptron Neural Network (MLPNN) with back propaga-tion procedure was developed for training the network. A Dataset containing 304 values of water pressure test in Granodioritic rock mass of Shoor-Jiroft Dam project was used to train and test the network with the Levenberg-Marquardt training algorithm. The results indicated that neural network forecast hydraulic conductivity considerably better than regression methods do.
N Salimi , M Fatemiaghda , M Teshnehlab , Y Sharafi ,
Volume 10, Issue 3 (2-2017)
Abstract
Landslides are natural hazards that make a lot of economical and life losses every year. Landslide hazard zonation maps can help to reduce these damages. Taleghan watershed is one the susceptible basin to landslide that has been studied. In this paper, landslide hazard zonation of the study area is performed at a scale of 1:50,000. To achieve this aim, layers information such as landslides distribution, slope, aspect, geology (lithology), distance from the faults and distance from rivers using artificial neural network-based Radial Basis Function (RBF) and perceptron neural network (MLP), has been studied. Principal of RBF method is similar to perceptron neural network (MLP), which its ability somewhat has been identified up to now and there are several structural differences between these two neural networks. The final results showed that the maps obtained from both methods are acceptable but the MLP method has a higher accuracy than the RBF method.
Tahereh Azari,
Volume 19, Issue 1 (6-2025)
Abstract
Accurately determining hydraulic parameter values is the first step in sustainably developing an aquifer. Since Theis (1935) introduced the type curve matching technique (TCMT), it has been used to estimate aquifer parameters from pumping test data. However, the TCMT is subject to graphical error. To eliminate this error, a multi-layer perceptron (MLP) artificial neural network (ANN) was developed as an alternative to the conventional TCMT. This MLP ANN models the Bourdet-Gringaten well function to determine fractured double porosity aquifer parameters. The MLP model was developed using a four-step protocol and trained using the backpropagation method and the Levenberg-Marquardt optimization algorithm for the well function of double-porosity aquifers. Through a trial-and-error procedure and by applying principal component analysis (PCA) to the training input data, the optimal network structure with the topology [3×6×3] is determined. We evaluated the validity of the developed network with synthetic and real field data. The network receives pumping test data and provides the user with aquifer parameter values. This network provides an automatic, fast procedure for determining double-porosity aquifer parameters, eliminating the graphical errors inherent in the conventional TCMT.
