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.

本研究旨在使用机器学习方法来预测隧道地质及其施工时间和成本。为此，已经利用了高斯过程回归（GPR），支持向量回归（SVR）和决策树（DT）。对加兰公路隧道的地质条件及其施工时间和成本进行了估算。此外，在从隧道的入口和出口侧建造约200 m后，使用工具中这些扇区的现场观测数据，将对未构造零件的所有先前预测结果进行更新。五重交叉验证已应用于评估每个模型的性能。所获得的模型用于预测实际场景中的施工时间和成本，并通过不同的统计评估标准研究了每种模型的准确性。最后，事实证明所有模型都具有较高的性能，并减少了隧道地​​质的不确定性。但是，与SVR和DT工具相比，GPR可提供更准确的结果。因此，我们建议使用GPR来预测隧道的未来水平中的地质，施工时间和成本。