###### Abstract  
During tunneling processes, disc cutters of a tunnel boring machine (TBM) usually need to be frequently and unexpectedly replaced. Regular inspections are needed to check disc cutters' status, which significantly reduces the work efficiency and increases the cost. This paper proposes a new prediction model based on TBM operational parameters and geological conditions that determines whether disc cutter replacement is needed. Firstly, an evaluation criterion for whether the cutters need to be replaced is constructed. Secondly, specific parameters related to the evaluation criterion are analyzed and 18 features are established on tunneling monitoring information. Then, the mapping model between the cutter replacement judgement and the established features is built based on a kernel support vector machine (KSVM). Finally, the data obtained from a Jilin water transport tunnel project is utilized to verify the performance of the proposed model. Test results show that the new model can obtain an average accuracy of 90.0% and an average F\({}_{1}\) score of 86.2% on field data prediction based on data from past tunneling days. Therefore, the proposed data-predictive model can be used in tunneling to accurately predict whether disc cutters need to be replaced before human judgment, and thereby greatly improve tunneling safety and efficiency. t 202211 2267 202211  
At present, there are three main methods proposed for obtaining cutter failure information, namely an inspection method, a sensor monitoring method, and a data-driven method. Regular inspection is the simplest and most reliable method, and thus it is the most widely used method in engineering projects. However, the inspection method is difficult to reflect cutter failures in time, which makes TBM unable to work with the best performance before inspections. Moreover, TBM shutdowns are required for cutter inspections to be carried out, generally at certain times of day or every few strokes. Usually, such downtime of TBMs consumes more than 15% of the tunneling time, greatly reducing the efficiency of the construction and increasing the cost. Unlike the manual inspection method, the sensor monitoring method allows real-time disc cutter status information to be obtained. However, since sensors are installed on the cutterhead, they are required to work in a harsh working environment of high temperature (70-90 \({}^{\circ}\)C), high humidity (80-90% Relative Humidity), and strong vibration (10-20 times the acceleration of gravity), thus resulting in bad performance with detection techniques based on light [13], lubricant addition [14], the magnetoresistive effect [15], or the eddy-current effect [16; 17]. Another disadvantage of the sensor monitoring method is that power supply, as well as communication and maintenance of the sensors, are difficult to implement. Therefore, with the sensor-based method, real-time cutter status observation is almost impossible to achieve during tunneling because of sensors' low reliability. Furthermore, the method is costly because sensors are expensive. For instance, Herrenknecht, a German TBM manufacturer, devised a disc cutter ring wear monitoring device that costs over USD 100,000, not including maintenance costs of damaged disc cutter wear sensors. Due to the disadvantages of both manual inspection and sensor monitoring methods, data-driven models have become more popular. The data-driven method can predict disc cutter consumption and make maintenance plans before tunneling starts. Generally, the data-driven method can be classified into three kinds--empirical methods [18; 19], mechanism-based methods [5; 9; 20; 21; 22; 23; 24; 25; 26], and tunneling-parameters-based methods [8; 12; 27; 28]. Empirical methods study the regression of cutter health indices and geological parameters based on historical data.