PECULIARITIES OF DEGRADATION IN LOESS SOILS' DEFORMATION AND STRENGTH PROPERTIES ON THE EXAMPLE OF DNIPROPETROVSK CITY

T. Mokritskaya

Authors

T. Mokritskaya Oles Honchar National University of Dnipropetrovsk (DNU) 36 K. Marcs Ave., Dnipropetrovsk, 49044 Ukraine

Keywords:

Abstract

Presentation of mathematical modeling results for the deformation and strength properties degradation process using the group method of data handling. Testing of this method had been applied to longitudinal data on the variability of the properties of the geological environment as an element of regional level natural – technogenic system. Research Objective: to establish the peculiarities of massif degradation on the example of the longitudinal data research on periglacial formation soils' properties using methods of stochastic and inductive modeling. Solution methods and software: stochastic modeling performed with methods of correlation and multiple regression analysis; inductive modeling – with group method of data handling. Software: STATIST (O. Honchar DNU), trial version of STATISTICA, customized program by Koryashkina L.S. (Candidate of physical-mathematical sciences, Associate Professor at O. Honchar DNU) Results. These peculiarities indicate that there is subordination between subsidence properties degradation and changes in aggregate content: this confirms the A.K. Larionov's theory about consecutive destruction of rock's aggregate system and decrease in coagulation-dispersion type connections as a result of increase of water film thickness during subsiding. Scientific innovation. Methods of inductive mathematical modeling applied to the description of change pattern in loess soils' exposed to technogenesis allows to objectively establish factorial variables whose changes affect the intensity of the process. Coordinates, soils' physical properties indicators were set as the factor variables. The evaluation of technogenic impact intensity is not performed, which greatly simplifies the solution. Practical significance. Inductive Modeling will enable more accurate predictions of strain.

References

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