Decision-making in tunneling using artificial intelligence tools

Abstract Given the frequent cost overruns and schedule delays associated with tunnel construction projects, it is imperative that a detailed estimation of both be developed and considered prior to starting construction. To this end, two artificial intelligence tools of Gaussian Process Regression (GPR) and Support Vector Regression (SVR) were used to forecast geology, construction time and construction costs of a road tunnel project. The initial training datasets applied in the prediction tools were accessed from the previously-constructed road tunnels and the pre-existing observations of the tunnel under consideration. Also, during the tunnel construction, more training datasets obtained in the constructed parts were added to the previous datasets and the pre-constructed predictions of the GPR and SVR tools were updated. Lastly, comparing the predictions made by the GPR and SVR tools with the actual mode of the tunnel, and comparing the pre-updating predictions with the post-updating ones, it was concluded that, the GPR and SVR tools have presented very good predictions and they have reduced the uncertainties regarding geology and construction time and costs to an acceptable level. But, the GPR tool has presented more accurate results than the SVR tool. Also, the updating procedure can significantly increase the predictions accuracy.