Work

Optimized Data Preprocessing and Model Selection for TBM Cutter Wear Prediction

Year: 2025

Type: article

Abstract: The estimation of disc cutter life is important due to the high equipment replacement cost in tunnel boring machines (TBMs). In this paper, we propose a comprehensive method for predicting disc cutter wear, integrating dimensionality reduction in shield tunneling parameters, data input preprocessing, and optimized model selection. First, we select core TBM operation parameters as model inputs through principal component analysis (PCA) and PCA directly. And, then, based on the characteristics of the operation parameters, we propose a data preprocessing method for time series models. This method uses excavation rings as units, providing a more accurate representation of the actual shield excavation process. Furthermore, by evaluating the prediction accuracy, training efficiency, and inference efficiency of various models, we demonstrate that the combined PCA data + Min Max Scaler + gated recurrent unit (GRU) method achieves the most accurate prediction results while offering real-time prediction capabilities.

Source: Coatings

DOI: 10.3390/coatings15050564

Authors: Liping Bai, Deyun Mo, Hongshu Li, Weiran Huang, Zelun Cai

Institutions: Macau University of Science and Technology, Lingnan Normal University, Guangdong University of Technology, Guangzhou Metro Group (China)

Language: English

Topic: Tunneling and Rock Mechanics

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $3,137