Hossein Montaseri; Reza Khalili; Mehdi Malek mahmudi
Abstract
Side weirs are flow diverting structures that are widely used in irrigation and drainage industry, flood control, sanitary engineering and urban sewage systems. So far, the discharge coefficient in this type of overflows has been determined experimentally using regression techniques, and for this reason, ...
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Side weirs are flow diverting structures that are widely used in irrigation and drainage industry, flood control, sanitary engineering and urban sewage systems. So far, the discharge coefficient in this type of overflows has been determined experimentally using regression techniques, and for this reason, in this research, linear regression models and artificial neural network were used to predict the discharge coefficient of the lateral overflow, and their results are in agreement with each other and with Computational values were compared and the best model in this field was selected for prediction. The discharge coefficient of lateral spillways is based on several combinations of dimensionless independent variables including the ratio of length to width of the spillway (L/B), the ratio of the flow depth downstream of the spillway to the height of the spillway (Hd/P), the ratio of the length of the spillway to the height of the spillway (L/ P), the ratio of weir discharge flow to upstream flow (Qw/Qu) and the ratio of flow depth upstream of weir to weir width (Hu/B) were predicted. ANN7 model with inputs L/P, Qw/Qu, hu/B has the highest value of regression coefficient equal to 0.92 and RMSE and MAE error values equal to 0.23 and 0.15 performed the best prediction. Reg1 model with the values of regression coefficient, RMSE and MAE equal to 0.72, 0.17 and 0.11, respectively, it created the best results in prediction and was selected as the best linear regression model. The general results showed that ANN models compared to linear regression produces better results.
nasser abootaleb; Mona Alizadeh Giashi
Volume 1, Issue 1 , June 2019, , Pages 10-18
Abstract
Failure and buckling in shell structures, including silos, can be created for various reasons such as distortion, defects created during construction, defects created during operation, or environmental factors, etc. Silos can be subjected to a variety of lateral and axial loads, but since they are in ...
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Failure and buckling in shell structures, including silos, can be created for various reasons such as distortion, defects created during construction, defects created during operation, or environmental factors, etc. Silos can be subjected to a variety of lateral and axial loads, but since they are in fact affected by the combination of these loads, silos behave under the influence of composite loads. The silos in this research are hot-rolled and located on the ground. The variables of this research are type and scope of defect. Silos are also evaluated in semi-filled and semi-solid state. The model silos were analyzed in ANSYS software. The radial radial neural network RBF was used to predict the buckling capacity of the cylindrical shells. The results of this study indicate that artificial neural networks are a good tool for predicting buckling capacity of silos with defects.
