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Modelling the cyclic swelling pressure of mudrock using artificial neural networks
The majority types of sedimentary rocks, according to their abundance, are mudrock (65 %), sandstone (20 - 25 %) and carbonate rocks (10 - 15 %). Mudrock is a general term for sediments composed mainly of silt and clay sized particles. Mudrock is mainly composed of clay minerals and silt-grade quartz grains. Other minerals may also be present. Organic materials may reach several percent and higher. Nodules commonly develop in mudrock, usually of calcite, dolomite, siderite or pyrite. Because of clay minerals in mudrocks, they absorb water easily and in large amounts. The stochastic nature of the cyclic swelling behaviour of mudrock and its dependence on a large number of interdependent parameters was modelled 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 modelling are presented in this paper.
Modelling the cyclic swelling pressure of mudrock using artificial neural networks
The majority types of sedimentary rocks, according to their abundance, are mudrock (65 %), sandstone (20 - 25 %) and carbonate rocks (10 - 15 %). Mudrock is a general term for sediments composed mainly of silt and clay sized particles. Mudrock is mainly composed of clay minerals and silt-grade quartz grains. Other minerals may also be present. Organic materials may reach several percent and higher. Nodules commonly develop in mudrock, usually of calcite, dolomite, siderite or pyrite. Because of clay minerals in mudrocks, they absorb water easily and in large amounts. The stochastic nature of the cyclic swelling behaviour of mudrock and its dependence on a large number of interdependent parameters was modelled 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 modelling are presented in this paper.
Modelling the cyclic swelling pressure of mudrock using artificial neural networks
Modellierung des zyklischen Quelldruckes von Schieferton mit künstlichen neuronalen Netzen
Moosavi, M. (author) / Yazdanpanah, M.J. (author) / Doostmohammadi, R. (author)
Engineering Geology ; 87 ; 178-194
2006
17 Seiten, 17 Bilder, 10 Tabellen, 37 Quellen
Article (Journal)
English
Modeling the cyclic swelling pressure of mudrock using artificial neural networks
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Modeling the cyclic swelling pressure of mudrock using artificial neural networks
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Textural and compositional controls on mudrock breakthrough pressure and permeability
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