Estimation method of earthwork excavation using shield tunneling data -- a case study of Chengdu Metro

Abstract

The occurrence of over-excavation or under-excavation in tunnel construction poses significant safety risks. Moreover, there is currently no automatic estimation method available for real-time estimation of earthwork excavation, particularly in the case of shield tunnels. In this study, we tracked the excavation process of Chengdu Metro Line 19, acquired tunneling parameters and earthwork excavation data using various sensors, and subsequently proposed an automatic estimation method that combines Bayesian optimization (BO) and gradient boosting regression tree (GBRT) algorithm. The results of our case study indicate that the BO-GBRT model improves the performance of earthwork excavation estimation, reducing the residual after each calculation with a root mean square error (RMSE) of 1.712 and mean absolute error (MAE) of 1.331. Furthermore, compared to other machine learning methods, the proposed BO-GBRT model demonstrates superior estimation performance. Additionally, the importance distribution of input parameters reveals that propulsion pressure, foam pressure, and rotation speed are the most critical factors affecting earthwork excavation. Overall, the proposed automatic estimation method shows great promise as a tool for efficiently estimating earthwork excavation in shield tunnel construction.