Catastrophic ground hazard failures over the past decade highlight the societal need to use more explicitly risk-based methods. A risk analysis procedure that fulfils validity, objectivity, consistency and adequacy is needed within the framework of geotechnical systems. In the design of geotechnical facilities, inherent uncertainties arise in the reduction of actual site geological-hydrogeological conditions to a representative analytical model, and in the determination of physical properties of the subsurface formations relevant to the site and the proposed geotechnical structure. Gravity, tectonic, weathering and erosion brought about by the environment are factors contributing eventually to the instability of geotechnical structures. Such factors are generally difficult to quantify with the present probabilistic approaches. Moreover, the associated rules and data in our current modeling are viewed as non-fuzzy. But, our preliminary investigations with geological maps, boreholes, field tests, sampling, laboratory work, up to the analysis and design, are all connected with vague, ill-defined, and to some extent non-statistical data. In this contribution a new procedure for risk evaluation in the design of geotechnical structures is presented using fuzzy uncertainty models. This contributes to the reduction of the difficulties mentioned above in quantifying the subjective geological and environmental data. The new technique includes basically the approaches of fuzzy quantification, synthetic fuzzy evaluation, and computation with imprecise and uncertain parameters utilizing the concept of fuzzy variables and fuzzy preference functions. The application of these models in the practice is simulated by numerical examples.


The uncertainties may be associated with physical phenomena that are inherently variable or with prediction and estimation of real state of nature performed under conditions of incomplete or inadequate information. From this perspective, uncertainty is associated with the inherent variability of the physical process and/or with the imperfection in the modeling of the physical process.

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