Updating Geological Conditions using Bayes Theorem and Markov Chain
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Due to cost constraints, geological conditions are investigated using boreholes. However, this means conditions are never known exactly, particularly for deep and long tunnels, because uncertainties exist between neighboring boreholes. Simulation can deal with underlying uncertainty, and offers benefits to project planners in the development of better alternatives and optimization. This research developed a simulation model using Bayes theorem and Markov chain, aiming to continuously update geological conditions of one-meter sections for tunnel construction, given the geological condition of the previous one-meter section is observed as construction progresses. An actual tunneling project is used as a case study to demonstrate the applicability of the developed methodology. The impacts are analyzed and discussed in detail. The simulation results show that continuous updates during construction can significantly improve prediction of project performance by eliminating uncertainty in the original assumption. The model can be expanded to predict results of future geologic exploration programs.