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Computers & Geosciences
The Forecasting of Rockburst in Deep-buried Tunnel with Adaptive Neural Network
IIS '09 Proceedings of the 2009 International Conference on Industrial and Information Systems
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
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Computers and Electronics in Agriculture
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Uncertain ground conditions represent the primary source of risk in underground tunnel construction. However, this problem can be solved by developing an accurate, probabilistic description of the geology. This paper presents a general model for probability based determination of tunnel geology that can be used as a basis for developing more effective decision support systems for tunneling design and construction. The proposed model is based on a Hidden Markov Model (HMM) and a neural network (NN). An approximate inference technique - a Particle Filter (PF) Algorithm - is used to simulate the geological parameters. This model overcomes the deficiencies of existing models by readily incorporating all available geologic information and updating geologic predictions based on observations given by the neural network. In order to validate the proposed model, the ''Drainage Water Tunnel Project'' at Zhong-He, Taipei, Taiwan was used. The results showed that the Neural-HMM model provides high accuracy in geological prediction.