ANN Model for Prediction of Rockfill Dam Slope Stability

Abstract

Dam safety and potential failure is one of the issues with the highest risk in water resources management. The dam slope stability is adversely influenced by the natural seepage process in the dam. Thus, monitoring of the pore and total pressures in the dam core is essential in the seepage process analysis. It is possible during the dam operation period to have one or more cells malfunctioning, after years of operation. Sometimes it is technically not possible to replace the cell or the costs of the replacement are too high and not economically justified. At the Pridvorica Dam, several instruments -cells for pore and total pressure monitoring malfunctioned. The objective of this study is to develop a neural network model for the prediction of the pore and total pressure on the malfunctioning cells and to demonstrate its quick and effective practical application for identifying complex non-linear relationship between the input and output variables. The proposed approach can be a very helpful tool for modeling of the stochastic behavior of the dam in order to give adequate warning of soil pressures to prevent failures.

High Embankment Dam Stability Analysis Using Artificial Neural Networks

“…Field measurement of pore pressure in an embankment dam can reliably provide information about ongoing seepage processes in the dam, soil mechanical properties of the dam body, and hydraulic conditions [8].…”

Evaluation Circular Failure of Soil Slopes Using Classification and Predictive Gene Expression Programming Schemes

“…This is regarded as among the most significant benefits of the standard soft computing methods used in today's world. In the literature on slope stability, successful implementations of these soft computing approaches may be identified (Wang et al, 2005;Choobbasti et al, 2009;Li et al, 2009;Chakraborty and Goswami, 2017;Kumar and Basudhar, 2018;Qian et al, 2019;Ahour et al, 2020;Li et al, 2020;Ray et al, 2020;Zheng et al, 2020;Che Mamat et al, 2021;Palazzolo et al, 2021); (Das et al, 2011;Erzin and Cetin, 2012;Erzin and Cetin, 2014;Abdalla et al, 2015;Ai and Zsaki, 2017;Chakraborty and Goswami, 2018;Rukhaiyar et al, 2018;Moayedi et al, 2019;Bui et al, 2020;Chen et al, 2020;He et al, 2020;Liao and Liao, 2020;Che Mamat et al, 2020;Markovic Brankovic et al, 2021;Meng et al, 2021).…”