Enhancing TBM operations under complex geological conditions using data-driven methods - Fu, Xianlei (Nanyang Technological University, 2023)
With the rapid pace of urbanization and increasing demand for underground space, tunnel construction gains popularity for its contribution to the subway transportation system. Tunnel Boring Machines (TBM) have been extensively applied in tunnel construction due to its efficiency, safety, and less impact on the ambient environment. During tunnel construction, the TBM monitoring system generates a huge volume of real-time data, which could be beneficial for improving the TBM performance during tunnel construction. However, due to complex geological conditions and knowledge gaps in TBM-soil interaction mechanism, there is a lack of an effective approach to extract useful knowledge from the data. As a result, the industry still heavily relies on human experience for TBM operations, which may impose risk on safety and compromise the performance of TBM. Therefore, the motivation of this Thesis is to establish a reliable approach that can enhance the overall TBM operation during excavation using data-driven methods.