SOIL TRANSITION MODELS | BTSYM


From case histories worldwide, it is evident that


quantification of the uncertainty of soil transition zones requires more attention to enable efficient tunneling performance. In current geotechnical and tunneling practice, the


soil transition location uncertainty is often addressed only qualitatively with a ‘?’ in ground profiles. These are incorporated into Geotechnical Baseline Reports (GBR), which are integral risk allocation documents for tunnel projects, and they are of key utility and significance when seeking to resolve claims over differing site conditions (DSC) and any financial litigation. Due to deterministic interpretations, such ground


profiles within the GBR provide only one, unique, boundary between soil units (SU). The ground profiles fail to include a level of confidence for the given soil transition locations and profiles, and, consequently, do not quantify the associated uncertainty in the longitudinal direction of the tunnel and at the cross sectional area of the TBM face. To get an appreciation of the large magnitude there


can be in DSC claims, in early 2014, Seattle Tunnel Partners, the Design-Build team constructing the Alaska Way Viaduct Tunnel, submitted a DSC claim worth US$20 million to the road tunnel project’s owner, Washington State Department of Transportation, after having completed only about 10% of the TBM drive. The consequences of risks and impact on the


community have skyrocketed with projects in complex environments. There is more at stake now than ever. It is our responsibility, as engineers, to find ways of dealing with uncertainty and build the community’s confidence in tunneling. There has been previous effort to quantify ground


uncertainty but not much work has been done on characterizing the subsurface features that are critical to tunneling. The research undertaken, therefore, is helping to move towards a more quantitative over qualitative assessments to include in the GBRs and so aid the tunnel contractor with more informed guidance in risk mitigation.


MODELING SPATIAL VARIABILITY AND UNCERTAINTY Geotechnical spatial variability is a result of different processes by which soils and rocks are deposited. Depending on the method of deposition, physical and engineering properties will vary in space within the deposit to different degrees. This inherent spatial variability is not completely captured even by rigorous site investigation programs. Underground construction and tunneling work


requires tools capable of capturing the variations and uncertainties in material properties. It is also critical to consider the scale at which spatial variability should be modeled. These spatial variations and uncertainties of


geological-geotechnical data can be addressed using geostatistics and random field theory.


These are, generally, two common approaches


to estimating the spatial correlation of geotechnical parameters: one uses semi-variance function from geostatistics; the other uses an autocorrelation function from random field theory and time series analysis. For the semi-variance function, the semivariogram


(also called the ‘variogram’) measures the average dissimilarity between two variables – such as between values of a parameter Z separated by a distance h, ie one value of Z at location u and another value at location (u+h). The variogram consists of the nugget (microscale variability and measurement error), sill (global variability in the direction of interest), and correlation length (also known as the range, which is the distance at which the data are no longer spatially correlated). For geotechnical engineering, and in particular for


tunneling applications, high quality knowledge of ground conditions governs the project procurement, progress, and success. Therefore, as engineers, we are interested in ground models that are geologically realistic, incorporate small-scale variability, and can help evaluate uncertainty from available investigation data. For this, a stochastic process is adopted – taking a


collection of random variables with multiple outcomes, which differ from one another within the set, in terms of properties of modeling techniques, but are considered equiprobable. Geostatistical techniques based on stochastic algorithms are used to estimate the spatial correlation/variability of geotechnical parameters. Applying these to borehole data has increasingly gained interest. This is particularly so for specific tests/ parameters, including standard penetration test (SPT), cone penetration test (CPT), soil stiffness, soil type, rock mass characterization. What parameters to model is still very much a research question. The research group at the Colorado School of Mines


have, since 2012, applied the results of geotechnical parameter spatial variability to help in various analyses, such as tunnel risk assessment, decision making, and optimizing risk mitigation plans. The general workflow for modeling the soil


conditions from borehole data uses the pluri-Gaussian simulation (PGSIM) technique, which is a type of stochastic algorithm. PGSIM is a complete approach as knowledge of a geological depositional environment can be applied towards simulations. The completeness of its approach comes from the ability of the technique to overcome most limitations on simulation methods – mainly of not capturing spatial changes in categories, such as geological unit proportions, contact relationships, and geological realism within realizations. Each outcome of the stochastic process is called a


‘realization’ and multiple such equiprobable results can be generated, with each one capturing the degree of heterogeneity and spatial variability of the stratigraphic units. The set of realizations differ from one another in terms of properties of modeling techniques but are


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