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Probabilistic Subsurface Modelling in Tunnelling Applications: Suggestions for Use in Practice
Abstract
Tunnel projects are often dominated by spatial uncertainty in geological-geotechnical conditions. In recent years, there has been an increasing interest in the use of geostatistical or random field methods for probabilistic subsurface modeling in tunneling applications. These methods provide the means for modeling the spatial variability and quantifying the spatial uncertainty in geological-geotechnical conditions. This, in turn, can help in characterizing geotechnical risk, though the extension from spatial uncertainty to tunneling risk is only emerging. This paper presents some examples of use cases from several tunneling projects globally to examine the applicability of such methods in tunneling practice. These use cases include geological modelling, spatial risk assessment quantification and mitigation planning. Both the advantages and limitations from a modelling standpoint are discussed, as well as the implications on various aspects of tunnelling projects including tenders, baseline reports and mitigation planning. The specific ways in which tunnelling risk is better quantified is clearly documented.
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.
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