A new index for cutter life evaluation and ensemble model for prediction of cutter wear

This paper proposed a new index for evaluation of disc cutter life during earth pressure balance (EPB) tunnelling. This new index was defined as the ratio of accumulated cutter radial wear to working time of the shield machine. With this new index, the measured disc cutter wear can be transformed into a time series data. To predict cutter wear with construction process, an ensemble intelligent model integrating one-dimensional convolutional neural network (1D-CNN) and gated recurrent unit (GRU) was developed via incorporating the proposed cutter wear index. A multi-step-forward prediction mode was adopted to train the ensemble model to predict cutter wear in advance. Field data collected from an EPB tunnelling section in Guangzhou-Foshan intercity railway, Guangzhou, China, was used for validation. Results showed that the proposed index and ensemble model can predict wear of a certain cutter with high accuracy. Three other sequential deep networks were employed for comparison to verify the applicability of the proposed index and ensemble model. The proposed index and ensemble model is convenient to be used on site and can predict wear of a certain cutter on cutterhead to help determine which cutter to be replaced during real-time construction.