Abstract
To optimize the prediction of structural geological conditions in the underground as of data collected at the surface, due to the usual great uncertainties involved, we discuss new perspectives for the construction of structural geological models, bearing in mind the common doubts involved and their implications in the safety of infrastructure works, mining, etc. This paper presents a statistical simulation applied to structural geological measures (dip-dip direction) obtained from schists during the design and construction of civil works through a correlation between surface data with different depth levels. Angular structural geological measures of joints and foliations converted in direction cosines were subjected to the PERMANOVA test to verify the amplitude of differences at different depth levels. The asymptotic results allowed to determine regions of confidence built around centroids through statistical simulation, allowable consistency was considered in regions where the differences in the simulated values were small enough from a practical point of view, considering that the difference between joint structures and foliation structures is smaller in the former. The foliation is a characteristic structure of rock deformation just like the joints.
Highlights
Seeking to understand the interaction between human activity and geological environment, aiming at positive or negative previsions in a project, and it is need of eventual prevention is the main focus of most of Geology Applied to Engineering, be it civil construction, mining, oil or environment (Hasui & Mioto 1992, Pastore et al 1998, Sadowski, 2014, among others)
10 This paper presents a statistical simulation applied to structural geological measures obtained from schists during the design and construction of civil works through a correlation between surface data with different depth levels
Angular structural geological measures of joints and foliations converted in direction cosines were subjected to the PERMANOVA test to verify the amplitude of differences at different depth levels
Summary
Seeking to understand the interaction between human activity and geological environment, aiming at positive or negative previsions in a project, and it is need of eventual prevention is the main focus of most of Geology Applied to Engineering, be it civil construction, mining, oil or environment (Hasui & Mioto 1992, Pastore et al 1998, Sadowski, 2014, among others). Geological risk analyses are performed through deterministic analysis of the geotechnical parameters These methods frequently do not quantitatively consider the randomness and variability of the parameters involved in the project, making it difficult to identity uncertainties (Hoek et al, 1997). In this case, the analysis does not demonstrate exactly the degree of predictability of the underground conditions of the works, in other words, the identification of the error percentage in the geological model. The main proposal of the present study is to use applied statistical analyses, to structural geological data, aiming to optimize the predictions of the geological conditions in depth. This study is applied in a practical example in order to try to answer the question: “Is it possible to know the data in depth using simulation?” In the search for an appropriate answer to this question, we use the Monte Carlo simulation, to better understand the correlation between the data collected in the field, on the surface, with those existing in-depth, which is difficult to predict and/or access
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