Current machine learning models for recognizing geological conditions during shield tunneling heavily rely on precise geological data labelling

limiting their applicability in complex geological environments. To address this

we propose a continuous dynamic time warping (CDTW)-based agglomerative hierarchical clustering model (CDTW-Agglomerative)

which integrates a linear interpolation framework to overcome DTW’s discretization issues. An online learning mechanism is implemented for dynamic strata recognition. The model’s accuracy and reliability are validated using Xiamen Metro Line 3 data

with generalization tested on Line 6 data. Results show recognition accuracies of 85% and 73% on the two datasets

demonstrating robust generalization. CDTW-Agglomerative outperforms DTW-Agglomerative

SoftDTW-Agglomerative

and CDTW-based models (K-means

K-medoids

Spectral clustering). Notably

it identifies cutterhead stratigraphy without requiring pre-labelled geological data

supporting intelligent decision-making for tunnelling parameters.