Open AccessNeural network modeling of safety system for construction equipment operation in permafrost zone
Author(s) -
Jamila Idrisova,
T. T. Kaverzneva,
N. V. Rumyantseva,
Igor Skripnik
Publication year - 2019
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/302/1/012128
Subject(s) - permafrost , artificial neural network , component (thermodynamics) , computer science , boundary (topology) , boundary value problem , environmental science , meteorology , geology , artificial intelligence , mathematics , mathematical analysis , oceanography , physics , thermodynamics
The problems of neural network modeling of working conditions securing system while operating constructional equipment in permafrost zone are considered. Determining the temperature fields distribution on the soil depth on air temperature in regions with harsh climatic conditions will allow us to solve a number of engineering problems directed to creation of safe working conditions at construction equipment operation. As a first approximation temperature on the surface and in the depth of the upper layer of earth changes under the periodic law, following the change of air temperature during the year. With soil depth increasing the amplitude of temperature fluctuations decreases, but a random component related to weather conditions (the impact of which could be significant) is added to the periodic component caused by change of a season. In the article the mathematical model in the form of Stefan’s problem in which boundary conditions on earth surface are replaced with results of measurements is considered. Methods of neural network creation of this problem solution and results of computing experiments are given. The received results show that neural networks are the flexible tool, allowing to consider featuring of a task and all available information. Thus accuracy of results corresponds to accuracy of initial information. The additional information can be effectively used for specification of the required decision.