Disc Cutter Wear Prediction Method of Hard Rock TBM Based on Bayesian Networks

摘要

Tunnel Boring Machine (TBM) has been widely used in various types of tunnel construction. The disc cutters are key component of TBM which affecting the construction duration, cost and safety of TBM tunnel significantly. According to site investigation and field data, the cutter wear mechanism is analyzed. The influence factors of cutter wear mainly include geological parameters, tunneling parameters and cutterhead design parameters. An initial cutter wear prediction network is established based on Bayesian theorem. The structure of the net (a Directed Acylic Graph, DAG) is formulated according to the inter-relationship among cutter wear and various factors. Prior Conditional Probability Table (CPT) is obtained by training the model with field data by Netica. As an illustration, a simplified network is applied to YHJW project. The cases test indicates that the method is feasible and the predicted values of cutter wear rate are basically equal to the data collected from YHJW south tunnel under the same geology and tunneling parameters. The predicted results can also reflect the regulation of disc cutter wear under the separate influence of Equivalent Quartz Content (EQC), Uniaxial Compressive Strength (UCS) and thrust. Finally, the conception of an intelligent TBM construction management platform is proposed.