جستجوی مقالات مرتبط با کلیدواژه "نروفازی" در نشریات گروه "عمران"

The estimation of hydraulic conductivity is one of the most important part of hydrogeological studies which is important in groundwater management. But due to practical, time or cost constraints, direct measurement is difficult. Hence, the using artificial intelligence models with low cost and high performance can be an appropriate alternative for this purpose. Since input data and different training techniques in these models are the most important source of uncertainty, the effect of various sources of uncertainty in output should be considered. In this research a Bayesian Model Averaging (BMA) are developed which includes the model combination of artificial neural network, fuzzy logic and neuro-fuzzy to estimate hydraulic conductivity and uncertainty analysis. In the BMA model, the weight of the models is determined by the Bayesian information criterion (BIC), and the within-model variance, steam from the uncertainty of input data and the between-model variance steam from uncertainty associated with the nature of the artificial intelligence model are calculated. In this study, the developed method has been applied to estimate the hydraulic conductivity in the Urmia aquifer. The results show that although the determination coefficient of BMA is not higher than the determination coefficient of the best model, the output of the BMA is the result of assigning weights that take into account the uncertainty between the models and the input data. Also, the effect of groundwater level variation on estimated hydraulic conductivity from pumpage test up to 2015 was evaluated and the result indicated an insignificant changes in hydraulic conductivity.

In this study, two general circulation models (GCMs) (Can-ESM2, BNU-ESM) were used to simulate the future precipitation of Tabriz city. The weakness of GCMs is the coarse resolution of climate variables in which the different methods of downscaling is about to solve this deficiency. In this study, the Artificial Intelligence (AI) models i.e., Artificial Neural Network (ANN) and Adaptive neuro fuzzy inference system (ANFIS) were used to statistically downscale the climate variables of GCMs. Without any doubt, the most important step during the use of these models, is selecting of the dominant inputs among huge of large-scale GCM data. So in this study for the selection of dominant inputs, decision tree and mutual information (MI) feature extraction methods were used. Also, the ensemble techniques were used to evaluate the efficiency of downscaling models and to decrease the uncertainties. Comparison the result of downscaling models indicated that the ensemble technique (i.e., hybrid of ANN and ANFIS) with dominant inputs based on decision tree feature extraction method presents better performance. In both GCMs, the application of the ensemble downscaling couple with dominant predictors based on decision tree model in precipitation downscaling showed 10%-38% increase in DC in versus the individual ANN and ANFIS downscaling models. The projection precipitation of Tabriz synoptic station for future (2020-2060) by proposed ensemble AI- based model indicated 30%-40% precipitation decreases under RCP4.5 and RCP8.5 scenarios.

Increasing traffic volume and creating traffic nodes in interurban and urban traffic networks will reduce the efficiency of the traffic network and the desired routes. Anticipating and discovering these traffic nodes as soon as possible can help solve the problem and streamline traffic flow. Artificial neural networks have shown that they are able to perform very well on their own learning capabilities. The main objective of this research is to predict and automatically detect traffic nodes using the intelligent neural network model and compare the model's performance with other models. By using educational data, the artificial neural network can be trained so that it can detect the desired output and perform predictive prediction on target data. The research method is to predict the network architecture from three input parameters and one output parameter. In this research, three types of artificial neural networks are used to predict and automatically detect traffic nodes. The data used in this research is the actual information of the traffic control center of Tehran-Karaj Freeway on a daily, weekly and monthly basis. Initially, the multilayer perceptron of artificial neural network was used, and another neural network used in this research was neuro-fuzzy network and eventually the neural network of the radial-based function was used to examine the success of the two previous networks. Based on the results, the efficiency and accuracy of different models based on the best and most comprehensive set of evaluation indicators were used to evaluate the performance of each of the models as well as to compare their efficiency with each other. Finally, the perceptron model was introduced with optimal efficiency. Comparison of the results of the traffic flow predicted values ​​with the measured values ​​in reality shows that the proposed model satisfies the traffic flow satisfactorily.

The traffic nodes are one of the most traffic-flow problems have been known to freeways. Discover the traffic nodes and fix are one of the main arteries, authorities and researchers in concerns has been. So presenting a suitable model for the detecting of nodes and taking necessary measures to accelerate the traffic-fiowing has certain importance. The purpose of the present research is forecasting and finding of automatically nodes by using neural network capabilities. Research methodology in this study is using three kinds of artificial neural network in order to discover the outcome of auto traffic nodes. That includes some laminated prspetron neural network, neural network of radial basis function and neural network neuro-fuzzy. The data which used in this research are actual data that obtained from control center of Tehran-Karaj freeway traffic daily, weekly and monthly. Finally the results which based on the evaluation indicators show that the perceptron network with three entrance parameters with two hidden layers of 15 neural in hidden layers has the best performance at comparing to the other models that optimize performance as the model which introduced.The research application is reducing the damages and problems which their causes are traffic nodes.

In-situ tests play important role in any geotechnical investigation. Pressuremeter test can be considered one of the most important in situ tests.This test can be considered one of the most important in situ tests in Geotechnical Engineering. This test is capable to properly estimate deflection parameters of soil. Three types of pressuremeters exist based on their placement in the boreholes: Predrilled pressuremeters (P.D.P), Self-boring pressuremeters (S.B.P) and Push-in pressuremeters (P.I.P). The Predrilled pressuremeters (P.D.P) have been used in this project. Based on expansion of a cylinder that is placed inside the borehole the pressure-volume variation during testing is recorded. In this research, the results of approximately 500 conducted Pressuremeter tests on the soils by Pajohesh Omran Rahvar Ltd (2006-2007) are employed. The number of tests decreased to 400 due to lack of accuracy and also high changes in the range of Pressuremeter modules. The tests have been carried out on the soils of Northwest Iran (Tabriz), South Iran (Kharg Island) and Northeast Iran (Mashhad). The Pressuremeter instrument used is menard pre-boring. Conducted tests in accordance with ASTM-D4719 represented acceptable accuracy. In the current paper, three types of Artificial Neural Network (ANN) are employed in interpretation of pressuremeter test results. First, multi layer perceptron neural network, one of the most applicable neural networks, is used. Then, neuro-fuzzy network, combination of neural-phase network is employed and finally radial basis function, a successful network in solving nonlinear problems, is applied. Neural network models showed prosperity to interpret Pressuremeter test. Soil physical and compaction properties are used in all these models. The applied models own 5 input parameters and 1 output parameters. Hidden layers with different number of neurons are tested in both one and two layers networks so as to select the most proper network architecture. It has been shown that a three-layer perceptron with differential transfer functions and sufficient number of neurons in hidden layer can approximate any nonlinear relationship. Consequently, one hidden layer is used in the present study. The neural network toolbox of MATLAB7.4, a popular numerical computation and visualization software, is used for training and testing of the MLPs. Transfer functions of networks are selected by trial and error. A large complex of carried out tests on the extensive range of fine and course grained soils is used as database. In order to determine the most exact network in the perceptron neural network, some networks with different architecture are employed. Of all neural network models, multi-layer perceptron neural network proved to be the most effective. However, other applied networks have shown favorable performance. Finally, different models have been compared and network with the most outstanding performance is determined. In order to evaluate the capability of model generalization, the performance of mentioned network against inexperienced data has been compared with empirical results. Contrary to conventional behavioral models, models based neural network do not demonstrate the effect of input parameters on output parameters. This research is response to this need through conducting sensitivity analysis on the optimal structure of proposed models. Also, derivation of governing equation for neural network model give more assurance to user to employ such models and consequently this facilitates the application of models in the engineering practices.