The results of laboratory model tests and numerical analysis on circular footings supported on sand bed under incremental

cyclic loads are presented. The incremental values of intensity of cyclic loads (loading, unloading and reloading) were applied

on the footing to evaluate the response of footing and also to obtain the value of elastic rebound of the footing corresponding

to each cycle of load. The effect of sand relative density of 42%, 62%, and 72% and different circular footing area of 25, 50,

and 100cm2 were investigated on the value of coefficient of elastic uniform compression of sand (CEUC). The results show that

the value of coefficient of elastic uniform compression of sand was increased by increasing the sand relative density while with

increase the footing area the value of coefficient of elastic uniform compression of sand was decreases. The responses of footing

and the quantitative variations of CEUC with footing area and soil relative density obtained from experimental results show a

good consistency with the obtained numerical result using “FLAC-3D”.

Dividing open channels are varied types of open channel structures used to provide water for irrigation channels, agriculture and wastewater networks. In the present study the mean velocity is calculated in different dividing angles within the branches channel through the use of artificial Neural Network (ANN) and coputational fluid dynamices (CFD) models. First the ANSYS-CFX model is used to simulate the flow pattern within the branch with a 90° angle. The results of the CFX model correspond fairly well to the results of the experimental model with Mean Absolute Percentage Error (MAPE) of 5%. After verifying, two CFX model are generated in 30° and 60° angle in different width ratios of 0.6, 0.8, 1, 1.2, and 1.4, and the mean velocities are obtained by flowmeter. Following that ANN model trained and tested through the use of a set of experimental and CFX datas. The comparison showed that the ANN model has an acceptable level of accuracy in predicting the dividing open channel mean flow velocity with mean value R2 of 0.93. Comparing the results indicated that ANN model with the MAPE of 1.8% performs better in 0.8 m width ratio. Also, in this width ratio the MAPE are equal to 1.58, 1.87, and 2.04 % in 30°, 60°, and 90° deviation angles respectively and therefore the model performs better in 30° angle.