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Surface subsidence trough predictions by artificial neural networksAmbrožič, Tomaž, 1962- ; Turk, GoranIn subsidence engineering true empirical or empirically based formulae are widely used to predict surface deformation due to undeground excavation. Our goal is to offer another prediction procedure ... by artificial neural networks (ANN). The training and testing of feed-forward multi layer neural network is based on available data. Input variables consist of wide extraction parameters while output variable consists of surface subsidence data. After the neural network has been successfully trained, and its performance tested on a separate testing set, it is used to predict the surface subsidence trough above the projected excavation. The results obtained by ANN are compared to those obtained by subsidence trough prediction equationsType of material - conference contributionPublish date - 1997Language - englishCOBISS.SI-ID - 255073
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Database name | Field | Year |
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Links to authors' personal bibliographies | Links to information on researchers in the SICRIS system |
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Ambrožič, Tomaž, 1962- | 12247 |
Turk, Goran | 08437 |
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