International Journal of Civil Engineering

en TIDAL LEVEL FORECASTING USING A TLRN ARTIFICIAL NEURAL NETWORKS Research Paper Research Paper In the present paper, ANN is used to predict the tidal level fluctuations, which is an important parameter in maritime areas. A time lagged recurrent network (TLRN) was used to train the ANN model. In this kind of networks, the problem is representation of the information in time instead of the information among the input patterns, as in the regular ANN models. Two sets of data were used to test the proposed model. San Francisco Bay tidal levels were used to test the performance of the model as a predictive tool. The second set of data was collected in Gouatr Bay in southeast of Iran. This data set was used to show the ability of the ANN model in predicting and completing of data in a station, which has a short period of records. Different model structures were used and compared with each other. In addition, an ARMA model was used to simulate time series data to compare the results with the ANN forecasts. Results proved that ANN can be used effectively in this field and satisfactory accuracy was found for the two examples. Based on this study, an operational real time environment could be achieved when using a trained forecasting neural network. Tidal level, TLRN, Artificial neural network, ARMA 48 54 http://ijce.iust.ac.ir/browse.php?a_code=A-10-3-264&slc_lang=en&sid=1 MISAGHI F. 180031947532846002075 180031947532846002075 No MOHAMMADI K. 180031947532846002076 180031947532846002076 Yes MOUSAVIZADEH M.H. 180031947532846002077 180031947532846002077 No

en ENERGY FORMULATION OF THE PIPE NETWORK ANALYSIS Research Paper Research Paper In this paper the analysis of the pipe networks is formulated as a nonlinear unconstrained optimization problem and solved by a general purpose optimization tool. The formulation is based on the minimization of the total potential energy of the network with respect to the nodal heads. An analogy with the analysis of the skeletal structures is used to derive tire formulation. The proposed formulation owes its significance for use in pipe network optimization algorithms. The ability and versatility of the method to simulate different pipe networks are numerically tested and the accuracy of the results is compared with direct network algorithms. Analysis, Optimization, Pipe Network 48 54 http://ijce.iust.ac.ir/browse.php?a_code=A-10-3-264&slc_lang=en&sid=1 AFSHAR M.H. 180031947532846002078 180031947532846002078 Yes