مقالات رزومه مهندس محمدعلی رحیمی

The change of land use and the reduction of permeable surfaces due to urban development have caused an increase in the peak discharge and volume of runoff due to rainfall, which causes flooding of roads and damage to urban areas. By accurately estimating the amount of runoff and properly designing the urban drainage network, as well as managing this runoff, the damages of urban floods can be reduced. SWMM rainfall-runoff model is one of the most efficient models in the design of drains and urban runoff management. Since the hydrological models must be evaluated before use, in this study the SWMM model was evaluated using 6 rainfall-runoff events for the city of Shahrood and the efficiency of the urban drainage network for floods with return periods of 2 to 15 years was investigated. For this purpose, six flood hydrographs of precipitation events was measured at the two main outlets of the city. he results showed that the SWMM model with the mean square root of the normalized error (NRMSE) of about 20% and R2= 0.90 in the validation stage has a suitable efficiency in simulating the flood hydrograph. Also, also the examination of the performance of urban drains showed for precipitation with a return period of 2, 5, 10 and 15 years, 9.1, 31.8, 36.4 and 41% of the canals are flooded, respectively.

The impact of flow on the bridge piers creates turbulent flows that cause scour around it, and as result collapse of the bridges. Despite to most research which done in this area, but due to the complexity of the phenomena and the many parameters involved in the phenomenon, there is still no exact relationship or fundamental solution to predict scour depth. In the present study, using the measured data, the scour depth was estimated with data mining methods such as artificial neural network, linear and nonlinear regression models and also empirical relationships. The data were used in two ways, with dimensions and non-dimension, which were obtained using dimensional analysis. The results showed that the artificial neural network model able to predict scour depth with determination coefficient (R2) equal to 0.97 and 0.81, as well as RMSE error equal to 0.06 m and 0.32, respectively, when data was used with dimension and non-dimension forms. Also, the empirical equations of the Colorado State University between the empirical relationships predicted scour depth with R2 and RMSE error equal to 0.84 and 0.52. Comparison of the results of different models shows that the best results are related to the artificial neural network model and it decreased error of prediction 70, 85.5 and 87.7% compared to linear regression model, nonlinear regression model and the empirical equation of the Colorado State University, respectively.