Experience forecast strain drawdown array of inductive modeling techniques


  • T. P. Mokritskaya Oles Honchar Dnepropetrovsk National University,
  • V. G. Batyr Oles Honchar Dnepropetrovsk National University,

Abstract

The article describes the experience of subsidence prediction of deformations of the array using the methods of inductive modeling. Inductive modeling methods, in particular methods of group account of arguments and neural networks can solve the problem of the forecast for the incomplete and irregular data on the state of a complex system. To  solve the problem        in the article involved the program trial – versions of Surfer, STATISTICA and authoring program Koryashkinoy L.S.  In   the course of solving the problem was carried out construction of spatial models of environment and its components (relief surfaces and capacities of individual horizons in the volume of the aeration zone, the surface level of underground water); models of behavior; predictive models subsidence. Construction of spatial models is performed in a scale of 1:25 000, in the conventional coordinate system, in an environment «Surfer». The data on the properties of the horizons of the zone of aeration were studied by statistical, correlation and regression types of stochastic analysis. The results of the primary analysis showed that the samples are heterogeneous and unbalanced by the values of skewness and kurtosis. Regression analysis showed that the use of stepwise regression method does not provide a significant multiple regression model. To forecast the relative subsidence were built inductive model of the relative depth of the subsidence and indicators of physical condition. According to the forecast values of subsidence relative to the regular grid of reference points in the depth interval specified capacity of the aeration zone, calculated the value of the total drawdown in a natural, middle and status of the array. The spatial position of the zones of change of the total drawdown indicates the position of the zones with different values forecast and realized risk. As a conclusion of the article can be said that the application of inductive modeling allows you to forecast changes in the natural subsidence with increasing humidity as a result of changes in the status of ground array.Key words: model, regression, drawdown, forecast 

Author Biographies

T. P. Mokritskaya, Oles Honchar Dnepropetrovsk National University,
Dnipropetrovsk National University Oles Gonchar,prof., Dr.  Geol. Sciences
V. G. Batyr, Oles Honchar Dnepropetrovsk National University,
Oles Honchar Dnepropetrovsk National University,student

References

Ivanov, I. P., Trzhicinskij, Ju.B., 2001. Inzhenernaja geo- dinamika [Engineering geodynamics]. Sankt-Peterburg: Science. (in Russian).

Kaloshina, S.V., Ponomarev,A.B., 2007. Analiz sushhestvu- jushhih metodov rascheta osadok zdanij I naprjazhenno- deformirovannogo sostojanija osnovanij [The analysis of existing methods for calculating the residue of buildings and stress-strain state grounds]. Vestnik Volg GASU, Ser.: Str-voiarhit., Vyp. 7 (26), 20 – 22 (in Russian).

Larionov, A. K., Priklonskij, V. A., Anan’ev, V. P., 1959. Lessovye porody SSSR i ih stroitel’nye svojstva [Loess USSR and their construction properties]. M.: Gosge- oltehizdat. (in Russian).

Mokrickaja, T. P., Korjashkina, L. S., 2013. Faktory I modeli degradacii prosadochnosti [Factors and model degrada- tion subsidence]. Bulletin of National Mining University, 4, 5 – 12. (in Russian).

Mokrickaja, T.P., 2015. K voprosu o prognoze degradacii prosadochnyh svojstv gruntov [On the issue of degra- dation of forecast subsidence of soil properties]. Dni- propetr. Univ. Bull. Ser.: Geol., geogr., 23(1), 90 –94. doi: 10.15421/111512 (in Russian).

Shimelevich, M.I., Obornev, E.A., 2008. Nejrosetevoj metod magnitotelluricheskogo monitoring geojelektricheskih parametrov sredy na osnove nepolnyh dannyh [Neural network method of magnetotelluric monitoring geoelectric parameters of the medium on the basis of incomplete data]. Vestnik KRAUNC. Nauki o Zemle, issue 11, 171
– 176 (in Russian).

Silkin, K. Ju., 2008. Geoinformacionnaja sistema Golden Software Surfer 8 [Geographic Information System Golden Software Surfer 8]. Voronezh: Izdatel’sko- poligraficheskij centr Voronezhskogo gosudarstvennogo universiteta. (in Russian).
Published
2016-07-01