###### Abstract
Mechanism-based methods use energy theory or mechanical analysis to model the rock breaking process, then the disc cutter wear is mapped with relevant parameters, such as cutterhead rotation speed [20], penetration rate [5], normal force of single disc cutter [23], uniaxial compressive strength (UCS) [9], Cerchar abrasivity index (CAI) [24], cutterhead topological structure [25], and so on. With the disc cutter wear, disc cutter failure can be easily predicted. One major disadvantage of both empirical and mechanism-based methods is that they rely highly on relatively stable geological parameters. However, the geology of tunnels is not immutable; these types of models are thus prone to generating significant errors in real-time prediction. Fortunately, tunneling-parameters-based prediction methods have proved that working data can reflect the interaction between TBM disc cutters and rocks forward. Benefiting from the current advancement of intelligitization of tunneling [29], TBM operational data can be collected and used in making predictions more effortlessly during tunneling. [PERSON] [27] established a fuzzy logical model to predict specific energy demand in the process of rock cutting with constant cross-section disc cutters based on a linear cutting test result database. [PERSON] [28] established a genetic programming-based wear life prediction model for shield disc cutters, using tree-shaped expression to reflect the relationship between cutter wear and relevant parameters (namely, rotating speed, excavation distance, penetration depth, disc cutter spacing width, and disc cutter installation radius). To estimate the life of the earth-pressure-balance shield machine, [PERSON] [8] proposed a genetic algorithm (GA) optimized group method of data handling (GMDH)-type neural network (NN) that used shield operational parameters (namely, thrust force, penetration rate, and cutter rotation speed) and geological conditions (i.e., uniaxial compressive strength) as model inputs. Meanwhile, after proposing a new disc cutter health index defined as the ratio of the rolling distance of disc cutters to their maximum rolling distance, [PERSON] [12] established a map between the new health index and 412 data features based on a one-dimensional convolutional neural network. These models focus on predicting the wear of each disc cutter. However, there is no need to monitor the status of each cutter during tunneling. For tool detection of a milling machine, the overall cutting performance is more concerned than the specific value of wear on each blade [30; 31; 32]. Likely, determining whether the cutting ability of the cutterhead has decreased is necessary to predict whether a disc cutter replacement is needed during tunneling, which is consistent with the human judgment standard. Therefore, the question of predicting disc cutter replacements is fundamentally a binary classification problem of a cutterhead's cutting performance. Considering the computational cost applied to projects, in this paper, a Gaussian kernel support vector machine prediction model is built for predicting disc cutter replacements using operational data and prior geological information. After being trained on a period of historical data, the proposed model can accurately predict in a real-time manner whether disc cutter replacement is needed, which reduces the time consumed by regular inspections. The main highlights of the proposed method are that it only takes the easily collected data as model input and complex cutter monitoring sensors are not needed; hence it can easily be deployed during construction. In addition, prior geological survey data are used to build the model, thereby it can acquire good performance in different tunneling projects. The rest of this paper is arranged as follows. Section 2 introduces a disc cutter replacement evaluation function and presents the proposed SVM method. Section 3 gives a review of the studied project and how the data was collected.