Modeling and Estimating the Uplift Force of Gravity Dams Using Finite Element and Artificial Neural Network Whale Optimization Algorithm Methods

Document Type : Research Article


1 candidated phd

2 Department of Water Engineering, Faculty of Agriculture


The correct identification of the uplift force plays an important role in the stability analysis of gravity dams. Therefore, it is very important to estimate it accurately. For this purpose, a numerical model of the foundation of a gravity dam of the Guangzhao, China was made using finite element method. After simulation, the uplift force values were obtained in different positions of drainage. Require experience, the timing of calculations and the accurate determination of the boundary conditions in numerical models, have caused to the development of the tendency to use intelligent models. For this purpose, in addition to the Artificial Neural Network model (ANN) with three-layer that consists of 4 input neurons, 1 hidden layer (with 8 neurons), and 1 output neurons, a new hybrid model of Artificial Neural Network-Whale Optimization Algorithm (ANN-WOA), was developed. The ratio of the parameters of the distance of the drain row from upstream dam, the distance from the center to center of drains, the drain diameter and the water surface upstream of the reservoir dam respect to the width of the dam foundation as input and relative uplift force were considered as output. The values of R2 , RMSE and RE% for the ANN-WOA model, were 0.998, 0.021 and 3.3%, respectively, and for the ANN model were 0.995, 0.261 and 4.67% respectively, that indicate the higher accuracy of the ANN-WOA model in the estimation of the uplift force than the ANN. In addition, the density plot box and the violin plot indicate that the point density and the probability distribution estimated data with the ANN-WOA model is very similar to that the data obtained from the numerical simulation compared with the ANN model.


Main Subjects

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