Prediction of curtain grouting efficiency based on ANFIS

As an important method for improving dam foundations, curtain grouting is designed to create a hydraulic barrier to decrease permeability, enhance strength, and reduce deformability of rock masses. To evaluate the improvement of rock masses, the Lugeon value (LU), rock quality designation (RQD), and fracture filled rate (FFR) after grouting are key evaluation indicators of grouting efficiency. A prediction method based on an adaptive neuro-fuzzy inference system is proposed to predict and evaluate curtain grouting efficiency in this study. Geological factors (fracture intensity, LU, and RQD before grouting), effective grouting operation factors (effective grouting pressure, effective grouting time, effective grout volume, and effective cement take), and tested interval depth are considered to be the critical factors that greatly influence the efficiency of curtain grouting and are selected as input parameters for prediction models. The grouting efficiency evaluation indicators (the LU value, RQD, and FFR after grouting) are selected as output parameters for evaluation of the efficiency. In addition, a formula for estimating the influence radius of grouting boreholes, which is used to determine the sphere of grouting influence, is proposed. To better reflect the influence of the position of grouting boreholes on the effects of grouting, this study suggests that the effective grouting operation factors can be calculated using an improved inverse distance weighting method. As a case study, this approach is used to predict the results of grouting and to evaluate the efficiency of curtain grouting in hydropower project A, located in the southwestern part of China. The approach shows considerable accuracy in predicting the results of grouting and evaluating grouting efficiency.