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A platform for research: civil engineering, architecture and urbanism
Risk Guided Strategy to Optimize Geotechnical Investigations on Tunnel Projects
https://orcid.org/http://orcid.org/0000-0002-8873-4126
The scope of geotechnical investigations in tunnel projects is generally driven by allocated resources rather than the expected ground variability. This research proposes a systematic and rational methodology for identifying priority locations of geotechnical investigations based on tunnel risks, site conditions, and project-related constraints. The methodology is applied to an actual soil tunnel project in an urban setting using preliminary geotechnical investigation data. The goal is to quantify the spatial uncertainty in soil abrasivity index (SAI), which is a geotechnical parameter that influences the risk of cutter tool wear. Stochastic geostatistical modeling algorithms are utilized to characterize the 3D spatial variability in soil units and SAI. Geospatial visualizations of SAI uncertainty and consequences of tool wear are employed to develop an R index map that highlights the impacts of uncertainty in tool wear rates. Project constraints such as drilling accessibility and budget are integrated into the R index map to identify priority locations for additional investigations. The study involves virtual sampling of additional boreholes to quantify the uncertainty reduction in tool wear rates and the locations where intervention by the tunnel boring machine (TBM) may be required. The results show that prioritized investigations targeting areas with high ground uncertainty can reduce the uncertainty in intervention locations by approximately 90 rings (equivalent to 160 m).
Risk Guided Strategy to Optimize Geotechnical Investigations on Tunnel Projects
https://orcid.org/http://orcid.org/0000-0002-8873-4126
The scope of geotechnical investigations in tunnel projects is generally driven by allocated resources rather than the expected ground variability. This research proposes a systematic and rational methodology for identifying priority locations of geotechnical investigations based on tunnel risks, site conditions, and project-related constraints. The methodology is applied to an actual soil tunnel project in an urban setting using preliminary geotechnical investigation data. The goal is to quantify the spatial uncertainty in soil abrasivity index (SAI), which is a geotechnical parameter that influences the risk of cutter tool wear. Stochastic geostatistical modeling algorithms are utilized to characterize the 3D spatial variability in soil units and SAI. Geospatial visualizations of SAI uncertainty and consequences of tool wear are employed to develop an R index map that highlights the impacts of uncertainty in tool wear rates. Project constraints such as drilling accessibility and budget are integrated into the R index map to identify priority locations for additional investigations. The study involves virtual sampling of additional boreholes to quantify the uncertainty reduction in tool wear rates and the locations where intervention by the tunnel boring machine (TBM) may be required. The results show that prioritized investigations targeting areas with high ground uncertainty can reduce the uncertainty in intervention locations by approximately 90 rings (equivalent to 160 m).
Risk Guided Strategy to Optimize Geotechnical Investigations on Tunnel Projects
https://orcid.org/http://orcid.org/0000-0002-8873-4126
The scope of geotechnical investigations in tunnel projects is generally driven by allocated resources rather than the expected ground variability. This research proposes a systematic and rational methodology for identifying priority locations of geotechnical investigations based on tunnel risks, site conditions, and project-related constraints. The methodology is applied to an actual soil tunnel project in an urban setting using preliminary geotechnical investigation data. The goal is to quantify the spatial uncertainty in soil abrasivity index (SAI), which is a geotechnical parameter that influences the risk of cutter tool wear. Stochastic geostatistical modeling algorithms are utilized to characterize the 3D spatial variability in soil units and SAI. Geospatial visualizations of SAI uncertainty and consequences of tool wear are employed to develop an R index map that highlights the impacts of uncertainty in tool wear rates. Project constraints such as drilling accessibility and budget are integrated into the R index map to identify priority locations for additional investigations. The study involves virtual sampling of additional boreholes to quantify the uncertainty reduction in tool wear rates and the locations where intervention by the tunnel boring machine (TBM) may be required. The results show that prioritized investigations targeting areas with high ground uncertainty can reduce the uncertainty in intervention locations by approximately 90 rings (equivalent to 160 m).
Risk Guided Strategy to Optimize Geotechnical Investigations on Tunnel Projects
Lecture Notes in Civil Engineering
Wu, Wei (editor) / Leung, Chun Fai (editor) / Zhou, Yingxin (editor) / Li, Xiaozhao (editor) / Gangrade, Rajat (author) / Mooney, Mike (author)
Conference of the Associated research Centers for the Urban Underground Space ; 2023 ; Boulevard, Singapore
Proceedings of the 18th Conference of the Associated Research Centers for the Urban Underground Space ; Chapter: 161 ; 1201-1203
2024-07-10
3 pages
Article/Chapter (Book)
Electronic Resource
English
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