The scope of geotechnical investigations in tunnel projects is generally driven by the allocated resources rather than the expected variability in the ground conditions. Uncertainties in ground conditions may lead to poor decisions in project planning and active risk management. This paper presents a systematic and rational methodology to identify priority locations of additional geotechnical investigations for soft ground tunneling applications based on tunnel risks, site conditions, and project-related constraints. The methodology is applied to the cutter tool wear risk and quantifies the uncertainty in soil abrasivity index (SAI), the relevant geotechnical parameter. Preliminary geotechnical investigation data and site conditions from an actual tunnel project in an urban environment are used to illustrate the proposed methodology and demonstrate its effectiveness. Pluri-Gaussian simulation and sequential Gaussian simulation are used to characterize the 3D spatial variability in soil units and soil abrasivity, respectively, along the tunnel alignment. The study integrates geospatial assessments of SAI uncertainty and consequences of tool wear to develop an R index map that delineates the impact of uncertainty in tool wear rate. Project constraints of drilling accessibility and budget are incorporated in the R index map to find locations of additional geotechnical investigations. The study simulates a virtual sampling of additional boreholes and quantifies the reduction in uncertainty in tool wear rate and tunnel boring machine (TBM) intervention locations. Additional investigation at priority locations is found to reduce cutterhead intervention location uncertainty by approximately 90 rings (160 m). Further, integrating the uncertainty in tool travel distance is found to influence the cutterhead intervention location uncertainty by approximately 40 rings.

隧道项目的岩土工程调查范围通常由分配的资源驱动，而不是由地面条件的预期可变性驱动。地面条件的不确定性可能导致项目规划和积极风险管理中的决策失误。本文提出了一种系统且合理的方法，用于根据隧道风险、现场条件和项目相关限制条件确定软土地隧道应用的额外岩土工程勘察的优先位置。该方法适用于刀具磨损风险，并量化了相关岩土参数土壤磨蚀性指数 (SAI) 的不确定性。来自城市环境中实际隧道项目的初步岩土调查数据和现场条件用于说明所提出的方法并证明其有效性。Pluri-Gaussian 模拟和顺序高斯模拟分别用于表征沿隧道路线的土壤单元和土壤磨蚀性的 3D 空间变异性。该研究整合了对 SAI 不确定性和工具磨损后果的地理空间评估，以开发一个描述工具磨损率不确定性影响的R指数图。钻探可达性和预算的项目限制被纳入R指数地图，以找到额外的岩土调查的位置。该研究模拟了额外钻孔的虚拟采样，并量化了工具磨损率和隧道掘进机 (TBM) 干预位置不确定性的降低。发现在优先位置进行的额外调查可将刀盘干预位置的不确定性降低约 90 个环（160 m）。此外，发现对刀具移动距离的不确定性进行积分会影响刀盘干预位置的不确定性大约 40 个环。