Tunnel ahead prospecting methods and intelligent interpretation of adverse geology: A review
Abstrak
Geological prospecting and the identification of adverse geological features are essential in tunnel construction, providing critical information to ensure safety and guide engineering decisions. As tunnel projects extend into deeper and more mountainous terrains, engineers face increasingly complex geological conditions, including high water pressure, intense geo-stress, elevated geothermal gradients, and active fault zones. These conditions pose substantial risks such as high-pressure water inrush, large-scale collapses, and tunnel boring machine (TBM) blockages. Addressing these challenges requires advanced detection technologies capable of long-distance, high-precision, and intelligent assessments of adverse geology. This paper presents a comprehensive review of recent advancements in tunnel geological ahead prospecting methods. It summarizes the fundamental principles, technical maturity, key challenges, development trends, and real-world applications of various detection techniques. Airborne and semi-airborne geophysical methods enable large-scale reconnaissance for initial surveys in complex terrain. Tunnel- and borehole-based approaches offer high-resolution detection during excavation, including seismic ahead prospecting (SAP), TBM rock-breaking source seismic methods, full-time-domain tunnel induced polarization (TIP), borehole electrical resistivity, and ground penetrating radar (GPR). To address scenarios involving multiple, coexisting adverse geologies, intelligent inversion and geological identification methods have been developed based on multi-source data fusion and artificial intelligence (AI) techniques. Overall, these advances significantly improve detection range, resolution, and geological characterization capabilities. The methods demonstrate strong adaptability to complex environments and provide reliable subsurface information, supporting safer and more efficient tunnel construction.
Penulis (7)
Shucai Li
Bin Liu
Lei Chen
Huaifeng Sun
Lichao Nie
Zhengyu Liu
Yuxiao Ren
Akses Cepat
PDF tidak tersedia langsung
Cek di sumber asli →- Tahun Terbit
- 2026
- Sumber Database
- DOAJ
- DOI
- 10.1016/j.jrmge.2025.05.032
- Akses
- Open Access ✓