A Multivariate Time Series Prediction Model for TBM Excavation Parameters Using a Convolution–GRU–Attention Neural Network
Year: 2026
Type: article
Abstract: Operating data from tunnel boring machines (TBMs) capture the state of both the machine and the ground, and accurate forecasting of their evolving operating variables is essential for assessing rock-mass stability and improving construction efficiency. However, it is difficult for the current methods to predict multivariate TBM driving parameters accurately. Therefore, a novel multivariable time series prediction method was proposed based on Convolution–GRU–Attention (CGA) neural networks. Initially, data preprocessing such as effective data extraction, segmentation, status judgment, and correlation analysis is applied to raw TBM excavation data to construct a parameter database encompassing 5987 TBM excavation cycles. Subsequently, the forecasting model is trained, incorporating techniques such as cross-validation, to ensure accurate predictions of excavation parameter trends. With the average coefficient of determination (R2) for total cutterhead thrust prediction reaching 0.883, and for cutterhead torque prediction achieving 0.923, the evaluation performance of the CGA model with a filter is better than GRU and BPNN. The results demonstrate that the proposed CGA model provides reliable predictions of key TBM operational parameters and offers useful insights into the evolution of TBM excavation behavior.
Source: Applied Sciences
Institution: Harbin Institute of Technology
Language: English
Topic: Tunneling and Rock Mechanics
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
Awards: G2275276279, G5090569880, G81678496
OpenAlex ID: W7138893720