M. Moosavia, , , M.J. Yazdanpanahb and R. Doostmohammadia


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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


Received 27 November 2005; 
revised 13 April 2006; 
accepted 4 July 2006. 
Available online 14 August 2006.

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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