Forecasting tunnel geology, construction time and costs using machine learning methods

This research intends to use machine learning approaches to predict tunnel geology and its construction time and costs. For this purpose, the Gaussian Process Regression (GPR), Support Vector Regression (SVR), and Decision Tree (DT) have been utilized. An estimation of the geological conditions of the Garan road tunnel and its construction time and cost has been conducted. In addition, after constructing about 200 m from the inlet and outlet sides of the tunnel, using the field-observed data of these sectors in the tools, all the previously forecasted results were updated for unconstructed parts. Fivefold cross-validation has been applied to assess the performance of each model. The obtained models are used to predict construction time and cost in real scenarios, and the accuracy of each model was investigated through different statistical evaluation criteria. Finally, it turns out that all the models provide relatively high performance and reduce the uncertainties of tunnel geology. However, the GPR provides more accurate results compared to the SVR and DT tools. Thus, we recommend the GPR for the prediction of geology and construction time and costs in future levels of a tunnel.