Prediction Model of Tunnel Boring Machine Disc Cutter Replacement Using Kernel Support Vector Machine

During tunneling processes, disc cutters of a tunnel boring machine (TBM) usually need to be frequently and unexpectedly replaced. Regular inspections are needed check cutters’ status, which significantly reduces the work efficiency increases cost. This paper proposes new prediction model based on TBM operational parameters geological conditions that determines whether cutter replacement is needed. Firstly, an evaluation criterion for replaced constructed. Secondly, specific related analyzed 18 features established monitoring information. Then, mapping between judgement built kernel support vector (KSVM). Finally, data obtained from Jilin water transport project utilized verify performance proposed model. Test results show can obtain average accuracy 90.0% F1 score 86.2% field past days. Therefore, data-predictive used in accurately predict before human judgment, thereby greatly improve safety efficiency.