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Modeling the cyclic swelling pressure of mudrock using artificial neural networks |
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| Authors: | M Moosavi MJ Yazdanpanah R Doostmohammadi |
| Institution: | aSchool of Mining Engineering, The University of Tehran, Iran bControl Center of Excellence and Department of Electrical and Computer Engineering, The University of Tehran, Iran |
| Abstract: | The stochastic nature of the cyclic swelling behavior of mudrock and its dependence on a large number of interdependent parameters was modeled using Time Delay Neural Networks (TDNNs). This method has facilitated predicting cyclic swelling pressure with an acceptable level of accuracy where developing a general mathematical model is almost impossible. A number of total pressure cells between shotcrete and concrete walls of the powerhouse cavern at Masjed–Soleiman Hydroelectric Powerhouse Project, South of Iran, where mudrock outcrops, confirmed a cyclic swelling pressure on the lining since 1999. In several locations, small cracks are generated which has raised doubts about long term stability of the powerhouse structure. This necessitated a study for predicting future swelling pressure. Considering the complexity of the interdependent parameters in this problem, TDNNs proved to be a powerful tool. The results of this modeling are presented in this paper. |
| Keywords: | Artificial neural networks Time delay neural networks Cyclic swelling pressure Cyclic wetting and drying Pressure cell |
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