Data-driven sequential development of geological cross-sections along tunnel trajectory

Forecasting geological cross-section ahead of tunnel face is an essential ingredient for tunnel design and construction. Geological analysis and drilling have been the most traditional approach for predicting tunnel ahead geological conditions. However, this practice is often subjective, and geological information retrieved from previous tunnel excavation in the same project has not been used quantitatively. In this study, a data-driven framework is proposed to sequentially develop geological cross-sections along planned tunnel trajectory conditioning on site-specific data and prior geological knowledge. The proposed framework dynamically and continuously incorporates geological information revealed from tunnel excavation as additional site-specific data, which provide first-hand direct geological information from the immediate past tunnel sections and can actually serve as the most relevant prior geological knowledge for forward prediction. All the prior geological knowledge is compiled as a site-specific training image database. When the actual geological cross-sections are revealed from tunnel excavation, the training image database is also updated for the next loop of tunnel ahead geological prediction. The proposed method is illustrated using data obtained from a real tunnelling project in Australia. Results indicate that the proposed method continuously provides accurate prediction of geological cross-sections along planned tunnel trajectory with quantified stratigraphic uncertainty.

Automated summary

A data-driven framework is proposed to predict geological cross-sections ahead of tunnel face. This method incorporates geological information revealed from tunnel excavation as additional data, providing accurate prediction of geological cross-sections along planned tunnel trajectory with quantified stratigraphic uncertainty.