Big data-driven TBM tunnel intelligent construction system with automated-compliance-checking (ACC) optimization

TBM Tunneling in subsea areas with adverse geological conditions presents potential risks due to non-compliant tunneling strategies resulting from manual adjustments and untimely communications. This study proposes an innovative subsea TBM tunnel intelligent construction system, featuring automated-compliance-checking (ACC) optimization, to assess feasibility through a case study. The key ingredient of the system is an integrated data-driven model framework comprising of a geological identification model based on fuzzy set theory and Dempster-Shafer (D-S) fusion theory and a machine learning-based TBM construction strategy ACC optimization model. In addition, a real-time data services scheme is established to manage TBM engineering big data, ensuring its effective utilization. Implemented in the PRDWRA project's F27 fault zone, the system identified greater stratum complexity than previously known, with potential dual faults. Post-ACC optimization, it achieved a 13.6% increase in advance rate, maintained consistent slurry pressure, and reduced the deviation between optimized and expected values by 57.1%. Finally, drawing upon statistical data sourced from project stakeholders, the system's deployment yielded a 9% cycle time reduction, and a 27% cost saving, with 17% from reduced construction times and 9% from lower accident rates. These results confirm the strategy's compliance with construction codes, significantly reducing risk and improving tunneling efficiency. The results confirm that the operational strategy adheres to construction code requirements, substantially reducing construction risks and effectively enhancing tunneling efficiency.