Data‐driven analysis of the spatial dependence of grouting efficiency during tunnel excavation

Abstract Grouting with water–cement mixtures is the most widely used and cost‐effective method for managing excess water inflow during tunnel construction. Due to uncertain geological and hydrological conditions, current grouting design relies heavily on the experience of onsite engineers. Recent advances in machine learning offer a promising alternative to traditional design to predict grout volume and improve grouting efficiency. Here, an artificial neural network (ANN) model was developed using the data set from an operation tunnel of Jurong Rock Caverns in Singapore to showcase an efficient and physics‐guided training strategy. The ANN model was refined by incorporating the spatial scenarios, including the number of grouting holes in four quadrants of tunneling faces, the sequence of grouting screens along the tunnel axis, and the order of grouting rounds on the tunneling faces. The results indicate that an improved training strategy should encompass the grouting process, from Round 1 with grouting holes uniformly distributed around the tunnel periphery, to Round 2 with grouting holes drilled midway between neighboring first‐round holes, and to Round 3 with grouting holes determined by onsite engineers. This model, trained based on the order of grouting rounds, performs better than the other models, highlighting the importance of establishing machine learning models grounded in physical principles. The finding was verified by the data set from another operation tunnel and concluded with a perspective on future grouting research.