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 exte...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,largescale 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,fulltime-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.展开更多
Accurate and rapid recognition of weathering degree(WD)and groundwater condition(GC)is essential for evaluating rock mass quality and conducting stability analyses in underground engineering.Conventional WD and GC rec...Accurate and rapid recognition of weathering degree(WD)and groundwater condition(GC)is essential for evaluating rock mass quality and conducting stability analyses in underground engineering.Conventional WD and GC recognition methods often rely on subjective evaluation by field experts,supplemented by field sampling and laboratory testing.These methods are frequently complex and timeconsuming,making it challenging to meet the rapidly evolving demands of underground engineering.Therefore,this study proposes a rock non-geometric parameter classification network(RNPC-net)to rapidly achieve the recognition and mapping ofWD and GC of tunnel faces.The hybrid feature extraction module(HFEM)in RNPC-net can fully extract,fuse,and utilize multi-scale features of images,enhancing the network's classification performance.Moreover,the designed adaptive weighting auxiliary classifier(AC)helps the network learn features more efficiently.Experimental results show that RNPC-net achieved classification accuracies of 0.8756 and 0.8710 for WD and GC,respectively,representing an improvement of approximately 2%e10%compared to other methods.Both quantitative and qualitative experiments confirm the effectiveness and superiority of RNPC-net.Furthermore,for WD and GC mapping,RNPC-net outperformed other methods by achieving the highest mean intersection over union(mIOU)across most tunnel faces.The mapping results closely align with measurements provided by field experts.The application of WD and GC mapping results to the rock mass rating(RMR)system achieved a transition from conventional qualitative to quantitative evaluation.This advancement enables more accurate and reliable rock mass quality evaluations,particularly under critical conditions of RMR.展开更多
Earthquakes are critical triggers for slope instability.While extensive research has been conducted on slope failure modes under seismic loading,the identification of sliding surface propagation and coalescence remain...Earthquakes are critical triggers for slope instability.While extensive research has been conducted on slope failure modes under seismic loading,the identification of sliding surface propagation and coalescence remains insufficiently explored.This study investigates the dynamic response of a deposit slope containing a weak interlayer through large-scale shaking table tests.The propagation process of the sliding surface was identified using the Hilbert-Huang transform and marginal spectrum analysis.Under seismic excitation,sliding occurs along the interface between the overburden and the weak interlayer,leading to sudden landslide events.Differential vibrations at the overburden-weak interlayer-bedrock interfaces are identified as a primary mechanism driving landslide initiation.As input acceleration increases,these interfacial vibration contrasts intensify,and the acceleration amplification effect within the overburden becomes markedly pronounced.Following landslide occurrence,the vibration differences across interfaces decrease sharply.In the time-frequency domain,seismic waves transmitted through the weak interlayer exhibit amplified low-frequency components.Marginal spectrum analysis of seismic energy evolution within the slope reveals that energy attenuation in the 19-22 Hz frequency band correlates with landslide occurrence,while attenuation in the 9-11 Hz band serves as an indicator for sliding surface propagation and coalescence.For seismic design of deposit slopes with weak interlayers,particular attention should be given to the increased seismic inertial forces in the overburden layer and the detrimental effects of low-frequency wave components on sliding surface development.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.52021005,52325904,and 51991391)。
文摘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,largescale 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,fulltime-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.
基金supported by the National Natural Science Foundation of China(Grant Nos.42077242 and 42171407)the Graduate Innovation Fund of Jilin University.
文摘Accurate and rapid recognition of weathering degree(WD)and groundwater condition(GC)is essential for evaluating rock mass quality and conducting stability analyses in underground engineering.Conventional WD and GC recognition methods often rely on subjective evaluation by field experts,supplemented by field sampling and laboratory testing.These methods are frequently complex and timeconsuming,making it challenging to meet the rapidly evolving demands of underground engineering.Therefore,this study proposes a rock non-geometric parameter classification network(RNPC-net)to rapidly achieve the recognition and mapping ofWD and GC of tunnel faces.The hybrid feature extraction module(HFEM)in RNPC-net can fully extract,fuse,and utilize multi-scale features of images,enhancing the network's classification performance.Moreover,the designed adaptive weighting auxiliary classifier(AC)helps the network learn features more efficiently.Experimental results show that RNPC-net achieved classification accuracies of 0.8756 and 0.8710 for WD and GC,respectively,representing an improvement of approximately 2%e10%compared to other methods.Both quantitative and qualitative experiments confirm the effectiveness and superiority of RNPC-net.Furthermore,for WD and GC mapping,RNPC-net outperformed other methods by achieving the highest mean intersection over union(mIOU)across most tunnel faces.The mapping results closely align with measurements provided by field experts.The application of WD and GC mapping results to the rock mass rating(RMR)system achieved a transition from conventional qualitative to quantitative evaluation.This advancement enables more accurate and reliable rock mass quality evaluations,particularly under critical conditions of RMR.
基金financially supported by the National Key Research&Development Program of China(2025YFE0123800)National Natural Science Foundation of China(No.52372343)+4 种基金Sichuan Transportation Science and Technology Project(2023-A-03)Applied Basic Research Programs of Science and Technology Department in Sichuan Province,China(2022NSFSC1086)Sichuan Science and Technology Program(2024YFHZ0121)the R&D Fund Project of China Academy of Railway Science Corporation Limited(K2024G008)National Natural Science Foundation of China(No.52502430).
文摘Earthquakes are critical triggers for slope instability.While extensive research has been conducted on slope failure modes under seismic loading,the identification of sliding surface propagation and coalescence remains insufficiently explored.This study investigates the dynamic response of a deposit slope containing a weak interlayer through large-scale shaking table tests.The propagation process of the sliding surface was identified using the Hilbert-Huang transform and marginal spectrum analysis.Under seismic excitation,sliding occurs along the interface between the overburden and the weak interlayer,leading to sudden landslide events.Differential vibrations at the overburden-weak interlayer-bedrock interfaces are identified as a primary mechanism driving landslide initiation.As input acceleration increases,these interfacial vibration contrasts intensify,and the acceleration amplification effect within the overburden becomes markedly pronounced.Following landslide occurrence,the vibration differences across interfaces decrease sharply.In the time-frequency domain,seismic waves transmitted through the weak interlayer exhibit amplified low-frequency components.Marginal spectrum analysis of seismic energy evolution within the slope reveals that energy attenuation in the 19-22 Hz frequency band correlates with landslide occurrence,while attenuation in the 9-11 Hz band serves as an indicator for sliding surface propagation and coalescence.For seismic design of deposit slopes with weak interlayers,particular attention should be given to the increased seismic inertial forces in the overburden layer and the detrimental effects of low-frequency wave components on sliding surface development.
