The use of new intelligent techniques in designing retaining walls

The stability of retaining walls against overturning is analyzed in this study using artificial intelligence methods. Five input parameters including wall height, wall thickness, soil friction angle, soil density, and stone cement mixture density were varied and 2000 cases were considered in developing the predictive models. Using the artificial neural network (ANN) method, eight prediction models were developed and evaluated based on the coefficient of determination (R²) and the root mean square error. R² values of 0.9740 and 0.9824 for training and testing datasets, respectively (for the best model), indicate the level of ANN capability in predicting safety factor (SF) of retaining walls. After developing the ANN model, the ant colony optimization (ACO) algorithm was used to maximize the safety factor of the wall by varying the input parameters. In fact, the best ANN model was selected to be used as a modeling function in ACO algorithm. The SF result from optimization section was obtained as 3.057 which show a significant difference from the mean SF values used in the modeling. It can be concluded that ACO may be used as a powerful optimization algorithm in optimizing SF results of retaining walls.