J. Grasmick’s scientific contributions

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.

Tunneling in karstic geology confronts numerous challenges due to unpredictable occurrence of voids. The current approach of karstic void risk assessment is qualitative or semi-quantitative and lacks consideration of the spatial variability and distribution of voids. This often influences the pricing strategies, and design and construction activities on tunnel projects. This paper presents a geostatistical modeling-based methodology to develop a quantitative assessment of karstic void risk for a tunnel project in a karstic geological setting. The methodology is applied on an actual mixed-ground tunnel project situated in a karstic geological environment in Malaysia. The geology at the tunnel project site consists of sedimentary rock formations with limestone as the predominant rock type overlain by weak sedimentary residual soils. Pluri-Gaussian simulation (PGS) technique, a stochastic geostatistical-modeling algorithm, is applied to characterize the spatial distribution of voids in 3D along tunnel alignment. Simulations from PGS take into consideration the anisotropic distribution of voids on the tunnel project site. PGS utilizes void data from borehole investigations to model different void sizes (Vs) as categorical variables. The variability in multiple realizations from PGS technique is used to quantify the uncertainty in occurrence probabilities, number, and frequency of karstic voids. The proposed methodology demonstrates the ability to develop probabilistic estimates of occurrence frequency of different void sizes. Probabilistic assessments indicating 95% confidence interval (CI) on number of voids and respective occurrence probabilities are presented. The probabilistic assessment results are applied to estimate the grout quantity required for void treatment, while considering uncertainty in void occurrence. A minimum, mean, and maximum cumulative grout volume of about 2000 m3, 4000 m3, and 8000 m3 (for 95% CI), respectively, is estimated along the alignment.