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• W3203652382 abstract "The application of geostatistical analyses of site investigation data for tunneling projects has gained significant attention in the literature. While the proposed techniques are useful for modeling the spatial variability and uncertainty in subsurface conditions, the extension of these techniques to the mitigation of critical tunneling risks in soils is limited. This paper demonstrates how a geostatistical approach can be used to characterize cutter tool wear and cutterhead clogging, two critical risks associated with mechanized tunneling in soils. A case study using data from the Seattle Northgate Link tunnel project is presented and validated by actual construction data reported. The results demonstrate that the proposed approach provides significantly more accurate prediction of the necessary mitigation measures than the conventional deterministic approach, providing significant estimated cost savings with a 40%–42% reduction in estimated tool replacements and a 17% reduction in estimated conditioning volume for this project." @default.
• W3203652382 created "2021-10-11" @default.
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• W3203652382 date "2021-12-01" @default.
• W3203652382 modified "2023-10-14" @default.
• W3203652382 title "A Probabilistic Geostatistics-Based Approach to Tunnel Boring Machine Cutter Tool Wear and Cutterhead Clogging Prediction" @default.
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• W3203652382 doi "https://doi.org/10.1061/(asce)gt.1943-5606.0002701" @default.
• W3203652382 hasPublicationYear "2021" @default.
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