A fuzzy model for high-speed railway tunnel convergence prediction in weak rock

Early prediction of tunnel diameter convergence is very crucial for a tunnel construction with the New Austrian Tunnelling Method (NATM) because it can help in any quick required adjustment of the design and consequently deadly hazards can be avoided. In this paper, a fuzzy model for the convergence prediction of high-speed railway tunnel was formulated based on Mamdani algorithm, triangular and trapezoidal membership functions using 135 datasets collected from a construction site in Hunan province (China). The inputs parameters included surrounding rock mass class index (SRM index), ground engineering conditions rating index (GEC index), tunnel overburden (H), rock density (d), distance between monitoring station and working face (D), and the elapsed time (T). The model performance was assessed by the variance account for (VAF), root mean square error (RMSE) indices and the coefficient of determination (R ²). In addition, a statistical regression analysis was performed for comparison purpose with the proposed model. The results showed overall good prediction accuracy. Finally a sensitivity analysis indicated that the SRM index and the GEC index were the most effective parameters controlling the convergence, while the density had least effect on the tunnel convergence.