Data-driven predictions of shield attitudes using Bayesian machine learning

Abstract

Shield attitude prediction is essential in shield tunnelling to prevent excessive attitude deviations and is highly correlated with shield operational parameters. Furthermore, geological and geotechnical uncertainty are the primary sources of attitude deviation risks; however, these uncertainties were neglected in previous studies. In this paper, a novel data-driven Bayesian machine learning method for site-specific shield attitude predictions is proposed by simultaneously considering the spatial variability of geotechnical parameters and the generic cross-correlations between shield attitude parameters and operational parameters. The Gaussian process model is first used to effectively capture the spatial variability of geotechnical parameters using field borehole data. The multivariate probability distribution model is then constructed to characterise the generic cross-correlation between four attitude parameters, four geotechnical parameters and eight shield operational parameters. In the Bayesian theory framework, a hybrid strategy is proposed to fuse the generic data from other shield tunnelling projects with the ongoing site-specific data, ensuring that the shield attitude prediction is dominated by site-specific data when those data are abundant or by generic data when the site-specific data are sparse. Finally, the Hamiltonian Monte Carlo sampling method is employed to generate the posterior distributions of shield attitudes conditioned on geotechnical parameters and shield operational parameters. The proposed method has the benefit of rigorously quantifying the uncertainties of shield attitude predictions. The proposed approach is examined on a real-world tunnelling project in Nanjing City, indicating that the proposed method outperforms existing techniques in terms of both accuracy and robustness. Parametric analyses are conducted to explore the impacts of key factors on the performance of the proposed approach.