Репозиторий Евразийского национального университета имени Л.Н. Гумилева
Репозиторий Евразийского национального университета имени Л.Н. Гумилева
Репозиторий Евразийского национального университета имени Л.Н. Гумилева
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Control and Analysis of Layered Soil Structure to Ensure Railway Roadbed Stability

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Автор
Bykov, Artem
Surzhik, Dmitry
Kassenova, Leila
Abzhanova, Ainagul
Svirina, Anastasia
Imanzhanova, Kulbarchin
Дата
2024
Редактор
Applied Sciences
ISSN
2076-3417
xmlui.dri2xhtml.METS-1.0.item-identifier-citation
Bykov, A.; Surzhik, D.; Kassenova, L.; Abzhanova, A.; Svirina, A.; Imanzhanova, K. Control and Analysis of Layered Soil Structure to Ensure Railway Roadbed Stability. Appl. Sci. 2024, 14, 8706. https:// doi.org/10.3390/app14198706
Аннотации
This article discusses a method for analyzing the layered structure of soil using the phasemetric method of geoelectric monitoring to ensure the reliability of a railway track. The importance of monitoring soil layers for timely detection of changes that may affect the stability and safety of railway tracks is emphasized. The use of geophysical monitoring methods, such as phase monitoring of the geoelectric signals, allows us to optimize measures to strengthen the roadway and increase its durability. The present article describes laboratory experiments in which a specialized setup was created to simulate the process of drilling through various soil layers. Geoelectric methods involving the registration of phase characteristics of the electromagnetic field were used in an experimental setup. The experiments demonstrated the effectiveness of the phase-metric method for determining the characteristics of the layered structure of the soil. The results showed that the change in the phase of the signal recorded at the receiving electrodes can be used to identify different soil layers with different electrical characteristics, such as moisture and density. The method of modeling the physical and geological environment using equivalent circuits of elements in the form of a dielectric made it possible to more accurately analyze the electrical properties of the soil. Based on the obtained data, an automatic monitoring system was developed using recurrent neural networks (RNNs), in particular long short-term memory (LSTM) networks, for automatic detection of bends and transitions in signal time series. Evaluation of the model’s effectiveness showed high accuracy in identifying layers, which contributes to increasing the reliability and efficiency of monitoring the condition of the railway track.
URI
http://repository.enu.kz/handle/enu/30038
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