Leakage prediction and post-grouting assessment in headrace tunnel of a hydropower project
Abstract
In the Himalayan region, tunnels are often constructed through complex and varying geological formations having rock mass with higher degree of jointing, faulting, folding, and weakness/shear zones. Such rock mass condition significantly increases the rock mass permeability which enables a higher possibility of water leakage into and out of the headrace tunnels built for hydropower projects and is a challenging situation for tunnel stability. Therefore, comprehensive leakage assessment and effective pre- and post-grouting application are essential in hydropower tunnels. In this research, the water leakage was predicted by using three machine learning approaches such as Support Vector Regression (SVR), Decision Tree (DT) regression, and K-Nearest Neighbors (KNN) models. The water leakage/inflow was predicted in one of the hydropower tunnels based on the geological condition of rock mass, rock mass quality, and hydro-geological conditions. The effective post-grouting method was applied to mitigate the potential water leakage and to enhance the rock mass quality and stability of the hydropower tunnel. It was observed that the injection grouting technique helps to make tunnels less permeable, reduces instability conditions, and ensures the long-term safety and structural integrity of the hydropower tunnels.