Data-driven estimation of TBM performance in soft soils using density-based spatial clustering and random forest

This study proposed a hybrid approach that integrates supervised and unsupervised learning to estimate the tunnel boring machine (TBM) performance in soft soil under limited geological information. By combining the shared nearest neighbor (SNN) algorithm and the density-based spatial clustering of applications with the noise (DBSCAN) method, an unsupervised learning approach, SNN-DBSCAN method, is performed to extract useful knowledge from the TBM logged data. The supervised random forest (RF) method is further combined with the SNN-DBSCAN method to predict the key TBM performance indicator. A realistic mass rapid transit (MRT) project in Singapore is adopted to examine the efficiency of the proposed methodology. The results from this case study indicate that: (1) The proposed SNN-DBSCAN method is suitable to perform data mining tasks on TBM logged data as the clustering result has an average of 85.03% similarity with site observation; (2) The knowledge extracted from the proposed approach could assist on soil identification as well as operational parameters determination; (3) Compared to the conventional RF method, the proposed approach achieves a high prediction accuracy with the coefficient of determination ( R 2 ) increasing from 0.78 to 0.92. • A data-driven prediction approach to estimate TBM performance in soft soils is proposed. • An unsupervised learning method, SNN-DBSCAN, is used to perform clustering analysis. • Corresponding random forest (RF) models are built to conduct prediction in different clusters. • A realistic tunnel case in Singapore is used to demonstrate its applicability and effectiveness. • It achieves an average improvement of 39% in MAE and 37% in RMSE compared to pure RF.