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 dissertation presents a general model of a Geology Prediction Support System (GPSS) that can be used as a basis for developing more effective microtunneling planning and construction. The GPSS engine, Neural-Hidden Markov Model (Neural-HMM), was designed using a combination of Hidden Markov Model and back propagation Neural Network. An approximate inference technique – Particle Filter (PF) Algorithm – is used to calculate and update posterior probability of 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 trained by the neural network. The GPSS is also equipped with a Value of Information module to support the decision maker with trade off information prior to extensive soil exploration. In order to validate and demonstrate the application of the proposed model a microtunneling project, the Zhong-He drainage water tunnel in Taiwan, was used as real case study. The results show that the GPSS provides a high accuracy of geology prediction and increases the value of information to decision makers.

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