EMNet: An ensemble deep learning approach for geological condition detection in tunnel excavation

Abstract

This paper established a novel ensemble deep learning method for the identification of the geological condition encountered during tunnel boring machine (TBM) operation. By integrating multiple MobileNet base models with information fusion through the Dempster-Shafer theory (DST), the proposed method is able to provide reliable identification of the geological condition based on the images of the excavated mucks on TBM’s conveyor belt. Shapley additive explanations (SHAP) analysis is further presented for model interpretation and understanding of the key features in the images. A case study using data collected from Singapore’s Circle Line 6 tunnel construction projects is conducted to verify the effectiveness of the proposed method. The results indicate that (1) The proposed method detects the encountered geological condition with high accuracy; (2) EMNet outperforms all the base MobileNet models; (3) SHAP analysis demonstrates that surface texture could be the key feature that assists the model for the classification. The developed method contributes to the state of knowledge in proposing a novel ensemble method for reliable image identification and the state of practice in proposing a useful tool to identify the encountered geological condition automatically.