An unsupervised incremental learning model to predict geological conditions for earth pressure balance shield tunneling

Abstract : Current machine learning models for predicting geological conditions during earth pressure balance (EPB) shield tunneling predominantly rely on accurate geological conditions as model label inputs. This study introduces an innovative approach for the real-time prediction of geological conditions in EPB shield tunneling by utilizing an unsupervised incremental learning model that integrates deep temporal clustering (DTC) with elastic weight consolidation (EWC). The model was trained and tested using data from an EPB shield tunneling project in Nanjing, China. Results demonstrate that the DTC model outperforms nine comparison models by clustering the entire dataset into four distinct groups representing various geological conditions without requiring labeled data. Additionally, integrating EWC into the DTC model significantly enhances its continuous learning capabilities, enabling automatic parameter updates with incoming data and facilitating the real-time recognition of geological conditions. Feature importance was evaluated using the feature elimination method and the Shapley additive explanations (SHAP) method, underscoring the critical roles of earth chamber pressure and cutterhead rotation speed in predicting geological conditions. The proposed EWC-DTC model demonstrates practical utility for EPB shield tunneling in complex environments.