Cutting-Edge Review of Big Data Preprocessing in TBM Tunnel Construction - Qinghua Guo; Haohan Xiao; Rongjian He; Lipeng Liu; Haopeng Yang; Hongtao Yu (Shock and Vibration)
Tunnel boring machines (TBMs) accumulate vast operational data crucial for analyzing complex rock-machine interactions during construction. Recent advancements in big data mining and machine learning (ML) have spurred significant artificial intelligence (AI) research in tunnel construction. The effectiveness of ML models depends on high-quality tunneling data, prompting increased research emphasis on big data preprocessing. This review synthesizes current practices in data preprocessing for tunnel construction, covering data characteristics, methods for handling anomalies such as discrete values, missing data, and noise. It also addresses challenges and future directions in big data processing. These findings establish a comprehensive foundation for further research in big data preprocessing and ML modeling in TBM construction.