About this Research Topic
Due to a growing number of projects in the tunneling construction industry, tunnel boring machines (TBMs) have been widely used for tunnel excavation because of their relatively higher efficiency, safety, and environmental friendliness compared to conventional blasting excavation. With the development of measurement and internet techniques, massive in-situ data of tunnel boring machines have been collected and recorded. This data not only records the tunnel boring machine's internal mechanism but also involves its interactions with the working environment. It is of great significance in improving tunnel boring machine design, analysis, operation, and maintenance level through mining these in-situ data.
In recent years, the amount of research about tunnel boring machine (TBM) in-situ data increased significantly, highlighting the application potential of in-situ data in TBM performance prediction, geological identification, and so on. This Research Topic focuses on the most recent advances in TBM in-situ data and provides readers with comprehensive information on the developments and achievements of in-situ data acquisition, pre-processing and management, in-situ data-driven TBM performance prediction, fault diagnosis and residual life prediction, in-situ data-driven geological recognition, and in-situ data-driven tunnel monitoring/risk management, which can offer innovative insights and perspectives for the development of in-situ data-driven techniques in TBM.
The main aim of this Research Topic is to bring together research and studies about TBM in-situ data covering but not limited to the topics of:
• Data acquisition system;
• Data pre-processing and management;
• TBM performance prediction;
• Fault diagnosis and residual life prediction;
• Data-driven operation, control, and maintenance;
• Geological prediction and recognition;
• Tunnel monitoring and risk management;
• Application of new machine learning techniques on TBM in-situ data.
In recent years, the amount of research about tunnel boring machine (TBM) in-situ data increased significantly, highlighting the application potential of in-situ data in TBM performance prediction, geological identification, and so on. This Research Topic focuses on the most recent advances in TBM in-situ data and provides readers with comprehensive information on the developments and achievements of in-situ data acquisition, pre-processing and management, in-situ data-driven TBM performance prediction, fault diagnosis and residual life prediction, in-situ data-driven geological recognition, and in-situ data-driven tunnel monitoring/risk management, which can offer innovative insights and perspectives for the development of in-situ data-driven techniques in TBM.
The main aim of this Research Topic is to bring together research and studies about TBM in-situ data covering but not limited to the topics of:
• Data acquisition system;
• Data pre-processing and management;
• TBM performance prediction;
• Fault diagnosis and residual life prediction;
• Data-driven operation, control, and maintenance;
• Geological prediction and recognition;
• Tunnel monitoring and risk management;
• Application of new machine learning techniques on TBM in-situ data.
Keywords: Tunnel boring machine, Big data, Data-driven techniques, Performance prediction, Fault diagnosis, Residual life prediction, Geological recognition, Risk management
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
