Estimation of convergence of a high-speed railway tunnel in weak rocks using an adaptive neuro-fuzzy inference system (ANFIS) approach

Estimation of tunnel diameter convergence is a very important issue for tunneling construction, especially when the new Austrian tunneling method (NATM) is adopted. For this purpose, a systematic convergence measurement is usually implemented to adjust the design during the whole construction, and consequently deadly hazards can be prevented. In this study, a new fuzzy model capable of predicting the diameter convergences of a high-speed railway tunnel was developed on the basis of adaptive neuro-fuzzy inference system (ANFIS) approach. The proposed model used more than 1 000 datasets collected from two different tunnels, i.e. Daguan tunnel No. 2 and Yaojia tunnel No. 1, which are part of a tunnel located in Hunan Province, China. Six Takagi-Sugeno fuzzy inference systems were constructed by using subtractive clustering method. The data obtained from Daguan tunnel No. 2 were used for model training, while the data from Yaojia tunnel No. 1 were employed to evaluate the performance of the model. The input parameters include surrounding rock masses (SRM) rating index, ground engineering conditions (GEC) rating index, tunnel overburden (H), rock density (), distance between monitoring station and working face (D), and elapsed time (T). The model’s performance was assessed by the variance account for (VAF), root mean square error (RMSE), mean absolute percentage error (MAPE) as well as the coefficient of determination (R) between measured and predicted data as recommended by many researchers. The results showed excellent prediction accuracy and it was suggested that the proposed model can be used to estimate the tunnel convergence and convergence velocity.