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Determining Local Scour Depth around the Cylindrical Pillars of Bridges Using Artificial Neural Network

Journal Name:

  • International Journal of Science and Engineering Investigations

Publication Year:

  • 2017

Key Words:

Author NameUniversity of Author
Abstract (2. Language): 
One of the most important reasons of destruction of bridges especially in case of flooding is local scour around pillars. Determining the depth of local scour around bridges pillars has an important role in designing bridges against this destructive phenomenon. This phenomenon has been investigated by different researchers using various methods but the main problem of all these methods is that all of them need a predetermined mathematical equation for modeling this complicated phenomenon. Existing equations for calculating the depth of scour have been all empirical up to now and the researchers in empirical proposed methods and formulas were likely to use two or more parameters in their relations and ignore some other ones. The depth of scour in pillars has been investigated in this paper using an artificial neural network model. Artificial neural network which is formed from a neurons and smart structure following existing neurons in the mind of human being, tries to simulate intracellular behavior of neurons in the brain through mathematical defined functions. This network has three input, hidden and output layers. The best used artificial neural network in this research have respectively 5, 9 and 1 neurons in their layers. After determining the mean of scour depth in pillars using network, target functions of MAE, RMSE and R2 have been used for comparison. Estimated values by model have been compared with some other methods (empirical formulas) and in order to determine the effectiveness of different parameters on the depth of scour, sensitivity analysis has been conducted that the results show that neural network model is one of the best methods for determining the depth of scour in pillars provided that it has adequate data to train network.
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