Journal of Engineering Geology

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(Paper pages 649-676) Engineering characteristics of alluvium and cemented materials of the slopes around the Milad Tower, and the results of slopes stability analyses under static condition is presented in this paper. Also in the paper, the feasibility of developing and using artificial neural networks (ANNS) for slope stability prediction is investigated. According to the geometry of slopes and strength and deformation properties of alluviums, factor of safety is calculated in 2D and 3D by PLAXIS7.2 and PLAXIS 3D Tunnel codes, respectively, and the results are also compared. In addition, stability of slopes is investigated through the use of MLP artificial neural networks (ANNs), which developed in MATLAB environment. The database used for development of the model comprises a series of 252 factor of safety for different slopes conditions (2D, 3D, flatted and 18 inclined from horizon at top of cut). The optimal ANN architecture (hidden nodes, transfer functions and training) is obtained by a trial-and-error approach in accordance to error indexes and real data. The input data for slope stability estimation consist of values of geotechnical and geometrical input parameters. As an output, the network estimates the factor of safety (FoS). The results indicate that the ANN model is able to accurately predict the FoS of the slopes.

Keywords: Slopes, Milad Tower, Factor of Safety, PLAXIS, Artificial Neural Networks

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