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  A new approach for the prediction of TBM penetration rate based on Particle Swarm Optimization (PSO)

  Artificial intelligence methods have been used in numerous research works to propose a TBM penetration rate prediction model. In this study, a combination of particle swarm optimization (PSO) technique and learning automata is used to propose a TBM penetration rate model. These techniques are used to assess the feasibility of developing a prediction model based on field data collected from Golab tunnel in Iran. In this study, learning automata is employed to adjust PSO parameters. Results show that using proper objective function offers better results. Two different PSO models were developed and will be introduced in this paper. The first model estimates TBM penetration rate as a linear function and the second one as a power function of rock properties. Evaluating the accuracy of these two models shows the power function method is superior to another method.

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