# One-Dimensional Convolutional Neural Network for Pipe Jacking EPB TBM Cutter Wear Prediction
## 2 Literature Review  
Empirical and semiempirical models have been provided by previous authors [10; 11; 12; 13; 14; 15; 16; 17; 18] to predict the cutter wear of TBMs. They consider rock parameters (CAI, UCS, quartz content, Vicker's hardness number (VHNR), fracturing degree, porosity, drilling rate index, and rock mass classification), machine properties (thrust, torque, penetration rate, rpm, and TBM diameter), and cutter geometry to estimate cutter tool life. However, they have drawbacks, such as requiring special laboratory equipment, working with a small number of input parameters, and having a high computational time due to theexperimental setup process. However, [PERSON] [19] provided a probabilistic approach using a regression-based empirical formula for tool life prediction. Moreover, they applied various multivariate regression analyses with geotechnical parameters (quartz content (EQC), soil strength (Tc), 60% passing soil size (D60), and standard penetration test (SPT)) for tool life estimation. Nevertheless, multivariable regression analysis is limited by the size of the input parameters. The probabilistic approach is not able to work with high-dimensional datasets. However, a TBM produces a large amount of data during excavation; thus, traditional regression analysis is not adequate for mechanized tunnelling evaluation. Therefore, many studies have been carried out to overcome the gap in the empirical formula and in the probabilistic models using artificial intelligence approaches. Most of the previous authors implemented artificial intelligence models for TBM penetration rate prediction [20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33]. Besides penetration rate prediction, artificial intelligence is rarely used for cutter wear prediction. [PERSON] et al. [34] established traditional machine learning models, such as Gaussian process regression, support vector regression, decision tree, and k-nearest neighbor, for cutter tool life prediction. In these applications, specific energy, quartz content, excavation depth, thrust force, cutter head rotation speed, penetration rate, screw rate, grouting pressure, and soil pressure were taken into account as the input, while disc cutter life (m\({}^{3}\)/cutter) was the output. However, these artificial intelligence models have disadvantages, such as overfitting, not being able to work with a large dataset, and memorizing the computational process. [PERSON] et al. [35] used the Norwegian University of Science and Technology (NTNU) cutter life index for cutter prediction. Rock mechanical properties (UCS, BTS, brittleness, and density) were used with an artificial neural network (ANN) and fuzzy logic algorithm. However, the NTNU-based model requires a specific laboratory setup to acquire cutter wear index. For this reason, [PERSON] et al. [36] provided a 1D CNN model for disc cutter life prediction and considered the operational parameters of a TBM. The proposed model is related to the new health index for real-time estimation based on the small rolling excavated distance and maximal rolling distance. Nevertheless, according to [PERSON] et al. [36], the proposed model had a shortcoming due to the disc cutters hardly reaching their wear limit for applications of the health index. Furthermore, the model requires the real maximal rolling distance, and if the real maximal rolling distance is not adequate, an alternative rolling distance should be created. Therefore, the model is complicated and not easy to implement. However, [PERSON] et al. [37] implemented the group method handling-genetic algorithm combination, with geological parameters and operational properties (UCS, Qc, RQD, PR, TH, GP, SP, and SE) as the input and Hf (m\({}^{3}\)/cutter) utilized as the output for the estimation model. The GMDH is a self-organizing method that can be applied to solve complex problems on nonlinear systems, despite it having drawbacks, such as being trapped in a local minimum and being unable to find a global minimum.