Vertical Shaft Dimension Optimization Using Modified BP Neural Network

Abstract

Abstract: The cross section dimensions have a significant impact on the fast construction and quality of the vertical shaft. The different cross sectional sizes are obtained by the numerical analysis using FLAC3D software and their associated total volume of the plastic zone, maximum stress and maximum deflection. The nonlinear relationships between the different shaft dimensions and the mechanical parameters obtained by FLAC3D are established through the modified Back Propagation (BP) neural network. The mechanical parameters that meet the design requirements are input into the trained BP neural network and the shaft cross section dimensions can be obtained throughout the back analysis.

Key words: BP Neural Network, Vertical Shaft Cross Section, Structural Optimization, FLAC^3D

LEI Gang. Vertical Shaft Dimension Optimization Using Modified BP Neural Network[J]. , 2014, 28(6): 64-66.

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Vertical Shaft Dimension Optimization Using Modified BP Neural Network

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