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Networks with back-propagation learning rule. Then, they were examined and analyzed for finding non-linear, complex relation between inputs and outputs using multi-layer perception neural The final architecture of the network used in this study are shown inįig. Output, include rock mass elasticity module and average horizontal in situ stress. 27 of 29 parameters, input, areĬlassified into three general categories of rock mass Geomechanical parameters, tunneling and convergence meter parameters. In order to perform intelligent back analysis on Chehel Chai Water Conveyance Tunnel using monitoring results, data were collected from 18 stations ofĬonvergence meter classified as 980 data categories each of which included 29 parameters (a total of 26460 parameters). Model for the Chehel Chai Water Conveyance Tunnel. Back analysis was performed using FLAC3Dĭ)All stations were modelled using Mohr-Coulomb elasto-plastic behavior model.Į)The dimensions of each monitoring station under study equals 10 meters of tunnel length in 5 meters before and after station site.
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Numerical back analysis presumptions of Chehel Chai Water Conveyance Tunnel monitoring stations are as follows:Ī)Variables: rock mass module of elasticity (Erm) and average horizontal in situ stress (Shav)ī)Back analysis by direct approach with periodic univariate algorithmĬ)Rock mass surrounding tunnel is placed in rock classes with close joints. Values of parameters regardless of their initial values. Where N: number of points measured uk: calculated displacement and uk*: measured displacementĪmong available optimization methods, univariate and univariate periodic search techniques can find optimal Optimization searching algorithm technique has been applied. However, the mentioned method needs a lot of time to carry out repetitive calculations.
#آموزش FLAC3D TRIAL#
Opposite the typical analysis however, the direct method is based on optimization in which trial values of unknowns areĬorrected in a way that the difference between values measured and calculated is minimized. In inverse method, mathematical formulation is just Problems may be solved in two different ways: inverse and direct. BACK ANALYSIS OF MONITORING STATIONS IN NARMAB WATER CONVEYANCE TUNNELīack analysis is able to forecast controlling parameters of system through analyzing its output behavior. Geomechanical properties of tunnel are as follows:ģ. Shematic representation of the geological profile of the Chehel Chai Water Conveyance Right coast of Narmab River (22675 meters).įigure 1. Furthermore, its entry altitude is 2324 meters, in west coast of Chehel Chai River, in Time-consuming and complex numerical back analysis of monitoring results.Ĭhehel Chai Water Conveyance Tunnel is 3175 meters long and 5 meters deep (excavationĭiameter).
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Therefore, it can be taken into account as an appropriate alternative for Teaching a neural network is a time-consuming process, when running at high speed, it can be Technique to be successful in solving problems related to geotechnical engineering. Neural Network abilities in learning from widely dispersed and erroneous data caused this Neural network that was able to regressive analyzing of displacements in future Convergence station By this way a model was mad based on artificial Neurons number and, activity functions obtained. Then according to network behavior in instructing step, optimum values for medial layers number, For network instruction, data bank of regressiveĪnalysis results of 18 Convergence stations was prepared in 980 classes by using FLAC3D Our input data were 27 parameters categorized in three classes including: tunneling data, geologicalĭata, and average of in situ horizontal stress. In this paper a model based on Perceptron multilayer artificial neural network have been presentedįor intelligent regressive analysis of Chehel Chai water conveyance tunnel base on monitoring data. Qom University of Technology, Qom, Iran – October 2015 Iranian Conference on Soil Mechanics and Foundation Engineering Intelligent back analysis using data from the instrument Masoud Ghaemi, Hamed zarei, Alireza Khalili, Kaveh ID: 2SMFE10103060207 Intelligent back analysis using data from the instrument (poster)