## 1 Introduction  
As the development of TBM (tunnel boring machine) technology and the continuous maturation of its core techniques, the application of TBMs in tunnel construction has become increasingly widespread. This has led to a substantial increase in the amount of excavation data collected, with a marked improvement in data accuracy. Effectively leveraging this data to propel TBM technology towards greater automation and intelligence has become a frontier topic in tunneling technology. However, the raw data generated during TBM operation is often highly complex and coupled, posing significant challenges in extracting valuable information while minimizing interference--key hurdles in achieving intelligent tunneling [1, 2, 3]. To address these challenges, researchers globally have extensively explored TBM data processing and model analysis. Data preprocessing is critical in TBM data analysis because raw data often contains noise and irrelevant information. For instance, [PERSON] et al. [4] employed the BIRCH (balanced iterative reducing and clustering using hierarchies) algorithm to efficiently compress massive TBM operation data, removing data points where torque, thrust, cutterhead rotation, or penetration equaled zero. [PERSON] et al. [5] applied K-nearest neighbors (KNN) for outlier detection and combined it with principal component analysis (PCA) for dimensionality reduction, thereby mitigating multicollinearity among features. [PERSON] et al. [6] proposed guidelines for efficient data filtering tailored to TBM operationsin hard rock, exploring various analytical and modeling techniques, including descriptive statistics, regression analysis, and neural network fitting. [PERSON] and [PERSON] (2017) utilized methods including standstill data removal, data grouping with mean calculations, outlier elimination, and uniform data point spacing. [PERSON] et al. (2018) used PCA to identify the parameters most correlated with redundant data, while [PERSON] et al. (2019) employed wavelet packet transform to denoise signals, retaining their temporal characteristics. [PERSON] et al. (2019) utilized smoothing methods and outlier detection to stabilize four key TBM indicators during the ramp-up phase of complete operational segments. [PERSON] et al. (2019) introduced an automated data processing approach that accurately segmented excavation cycles by analyzing parameters such as spindle rotation speed, advance rate, and downtime. [PERSON] et al. (2019) effectively removed noise in time-series data through smoothing techniques, improving data clarity and predictability. Their study indicated that achieving a balance between noise reduction and preserving authentic data characteristics is critical, as excessive smoothing may obscure significant details, while insufficient smoothing could retain excessive noise, reducing model performance. Because label noise in geological data obtained from field investigations presents challenges for accurate predictions. [PERSON] et al. (2019) proposed a confidence learning-based support vector machine (CL-SVM) model to eliminate label noise, enhancing the reliability of geological classification. Despite significant progress in preprocessing methods to improve model performance, limitations persist when addressing complex datasets. In the realm of model analysis, machine learning, empirical models, and traditional theoretical models are prominent research directions ([PERSON] et al., 2019; [PERSON] et al., 2019; [PERSON] et al., 2019). In machine learning, [PERSON] et al. (2019) applied supervised methods such as KNN, chi-squared automatic interaction detection (CHAID), support vector machines (SVM), classification and regression trees (CART), and neural networks (NN) to predict TBM penetration rate (PR). Their findings identified uniaxial compressive strength as the most critical factor and cutterhead rotation speed as the least significant. [PERSON] et al.