6 research outputs found

    Π€Π°ΠΊΡ‚ΠΎΡ€Ρ‹, ΠΎΠΊΠ°Π·Ρ‹Π²Π°ΡŽΡ‰ΠΈΠ΅ влияниС Π½Π° Ρ€Π°Π·Π²ΠΈΡ‚ΠΈΠ΅ ΠΏΡ€ΠΎΠ΄ΠΎΠ»ΡŒΠ½Ρ‹Ρ… ΠΏΡ€ΠΎΡ„ΠΈΠ»ΡŒΠ½Ρ‹Ρ… Π΄Π΅Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΠΉ зСмляного ΠΏΠΎΠ»ΠΎΡ‚Π½Π° Π² ΠΊΡ€ΠΈΠΎΠ»ΠΈΡ‚ΠΎΠ·ΠΎΠ½Π΅

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    Up to 2025 operations at the network of the Northern Railway (a subsidiary of JSC Russian Railways) may increase according to forecasts by 28,4 %, which is associated with construction of the Northern latitudinal railway. At the same time, a significant part of the Northern Railway is characterized by difficult climatic conditions: permafrost, polygonal-vein ice, peat bog areas, sharp temperature drops, significant amounts of precipitation in the form of snow. In the context of the planned increase in cargo intensity, the diagnostics of the roadbed in the zone of distribution of soils with weak bearing capacity against the backdrop of global climate change is of key character. The article is devoted to survey of the roadbed located in the permafrost zone. The results of diagnostics of the state of the railway track make it possible to forecast the state of railway infrastructure facilities, to categorize subsidence of the roadbed according to the degree of danger, and to develop measures for its stabilization. The objective of the work is to study the factors affecting degradation of the roadbed located in the permafrost zone. The methods of the work are based on field examinations of Β«sickΒ» places of the roadbed and statistical forms of analysis of longitudinal profile deformations (subsidence) of the track. The result of this work is the study of influence of a number of factors on dev elopment of deformations of the roadbed located in the permafrost zone. In the future, it is planned, based on the results of diagnostics of the state of the railway track, to forecast the permafrost state of railway infrastructure facilities, to categorize subsidence of the roadbed according to the degree of danger, and to develop measures to stabilize it.Π’ пСрспСктивС Π΄ΠΎ 2025 Π³ΠΎΠ΄Π° Π½Π° ΠΏΠΎΠ»ΠΈΠ³ΠΎΠ½Π΅ Π‘Π΅Π²Π΅Ρ€Π½ΠΎΠΉ ΠΆΠ΅Π»Π΅Π·Π½ΠΎΠΉ Π΄ΠΎΡ€ΠΎΠ³ΠΈ прогнозируСтся ΡƒΠ²Π΅Π»ΠΈΡ‡Π΅Π½ΠΈΠ΅ ΠΎΠ±ΡŠΡ‘ΠΌΠΎΠ² Ρ€Π°Π±ΠΎΡ‚Ρ‹ Π½Π° 28,4 %, Ρ‡Ρ‚ΠΎ связано со ΡΡ‚Ρ€ΠΎΠΈΡ‚Π΅Π»ΡŒΡΡ‚Π²ΠΎΠΌ Π‘Π΅Π²Π΅Ρ€Π½ΠΎΠ³ΠΎ ΡˆΠΈΡ€ΠΎΡ‚Π½ΠΎΠ³ΠΎ Ρ…ΠΎΠ΄Π°. ΠŸΡ€ΠΈ этом Π·Π½Π°Ρ‡ΠΈΡ‚Π΅Π»ΡŒΠ½Π°Ρ Ρ‡Π°ΡΡ‚ΡŒ Π‘Π΅Π²Π΅Ρ€Π½ΠΎΠΉ ΠΆΠ΅Π»Π΅Π·Π½ΠΎΠΉ Π΄ΠΎΡ€ΠΎΠ³ΠΈ характСризуСтся слоТными ΠΏΡ€ΠΈΡ€ΠΎΠ΄Π½ΠΎ-климатичСскими условиями: Π²Π΅Ρ‡Π½ΠΎΠΉ ΠΌΠ΅Ρ€Π·Π»ΠΎΡ‚ΠΎΠΉ, полигонально-ΠΆΠΈΠ»ΡŒΠ½Ρ‹ΠΌΠΈ льдами, Π·Π°Ρ‚ΠΎΡ€Ρ„ΠΎΠ²Π°Π½Π½ΠΎΡΡ‚ΡŒΡŽ Ρ‚Π΅Ρ€Ρ€ΠΈΡ‚ΠΎΡ€ΠΈΠΉ, Ρ€Π΅Π·ΠΊΠΈΠΌΠΈ ΠΏΠ΅Ρ€Π΅ΠΏΠ°Π΄Π°ΠΌΠΈ Ρ‚Π΅ΠΌΠΏΠ΅Ρ€Π°Ρ‚ΡƒΡ€, Π·Π½Π°Ρ‡ΠΈΡ‚Π΅Π»ΡŒΠ½Ρ‹ΠΌΠΈ ΠΎΠ±ΡŠΡ‘ΠΌΠ°ΠΌΠΈ осадков Π² Π²ΠΈΠ΄Π΅ снСга. Π’ условиях ΠΏΠ»Π°Π½ΠΈΡ€ΡƒΠ΅ΠΌΠΎΠ³ΠΎ роста грузонапряТённости диагностика зСмляного ΠΏΠΎΠ»ΠΎΡ‚Π½Π° Π² Π·ΠΎΠ½Π΅ распространСния Π³Ρ€ΡƒΠ½Ρ‚ΠΎΠ² со слабой нСсущСй ΡΠΏΠΎΡΠΎΠ±Π½ΠΎΡΡ‚ΡŒΡŽ Π½Π° Ρ„ΠΎΠ½Π΅ глобального измСнСния ΠΊΠ»ΠΈΠΌΠ°Ρ‚Π° носит ΠΊΠ»ΡŽΡ‡Π΅Π²ΠΎΠΉ Ρ…Π°Ρ€Π°ΠΊΡ‚Π΅Ρ€. Π‘Ρ‚Π°Ρ‚ΡŒΡ посвящСна вопросам обслСдования зСмляного ΠΏΠΎΠ»ΠΎΡ‚Π½Π°, располоТСнного Π² ΠΊΡ€ΠΈΠΎΠ»ΠΈΡ‚ΠΎΠ·ΠΎΠ½Π΅. Π Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹ диагностики состояния ΠΆΠ΅Π»Π΅Π·Π½ΠΎΠ΄ΠΎΡ€ΠΎΠΆΠ½ΠΎΠ³ΠΎ ΠΏΡƒΡ‚ΠΈ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡŽΡ‚ ΠΏΡ€ΠΎΠ³Π½ΠΎΠ·ΠΈΡ€ΠΎΠ²Π°Ρ‚ΡŒ состояниС ΠΎΠ±ΡŠΠ΅ΠΊΡ‚ΠΎΠ² ΠΆΠ΅Π»Π΅Π·Π½ΠΎΠ΄ΠΎΡ€ΠΎΠΆΠ½ΠΎΠΉ инфраструктуры, ΠΏΡ€ΠΎΠΈΠ·Π²ΠΎΠ΄ΠΈΡ‚ΡŒ ΠΊΠ°Ρ‚Π΅Π³ΠΎΡ€ΠΈΡ€ΠΎΠ²Π°Π½ΠΈΠ΅ просадок зСмляного ΠΏΠΎΠ»ΠΎΡ‚Π½Π° ΠΏΠΎ стСпСни опасности ΠΈ Ρ€Π°Π·Ρ€Π°Π±Π°Ρ‚Ρ‹Π²Π°Ρ‚ΡŒ мСроприятия ΠΏΠΎ Π΅Π³ΠΎ стабилизации. ЦСлью Ρ€Π°Π±ΠΎΡ‚Ρ‹ являСтся ΠΈΠ·ΡƒΡ‡Π΅Π½ΠΈΠ΅ Ρ„Π°ΠΊΡ‚ΠΎΡ€ΠΎΠ², Π²Π»ΠΈΡΡŽΡ‰ΠΈΡ… Π½Π° процСссы Π΄Π΅Π³Ρ€Π°Π΄Π°Ρ†ΠΈΠΈ зСмляного ΠΏΠΎΠ»ΠΎΡ‚Π½Π°, располоТСнного Π² ΠΊΡ€ΠΈΠΎΠ»ΠΈΡ‚ΠΎΠ·ΠΎΠ½Π΅. ΠœΠ΅Ρ‚ΠΎΠ΄Ρ‹ Ρ€Π°Π±ΠΎΡ‚Ρ‹ основаны Π½Π° Π½Π°Ρ‚ΡƒΡ€Π½Ρ‹Ρ… обслСдованиях Β«Π±ΠΎΠ»ΡŒΠ½Ρ‹Ρ…Β» мСст зСмляного ΠΏΠΎΠ»ΠΎΡ‚Π½Π° ΠΈ статистичСских Ρ„ΠΎΡ€ΠΌΠ°Ρ… Π°Π½Π°Π»ΠΈΠ·Π° ΠΏΡ€ΠΎΠ΄ΠΎΠ»ΡŒΠ½Ρ‹Ρ… ΠΏΡ€ΠΎΡ„ΠΈΠ»ΡŒΠ½Ρ‹Ρ… Π΄Π΅Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΠΉ (просадок) ΠΏΡƒΡ‚ΠΈ. Π Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚ΠΎΠΌ ΠΏΡ€ΠΎΠ²Π΅Π΄Ρ‘Π½Π½ΠΎΠΉ Ρ€Π°Π±ΠΎΡ‚Ρ‹ являСтся исслСдованиС влияния ряда Ρ„Π°ΠΊΡ‚ΠΎΡ€ΠΎΠ², Π½Π° Ρ€Π°Π·Π²ΠΈΡ‚ΠΈΠ΅ Π΄Π΅Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΠΉ зСмляного ΠΏΠΎΠ»ΠΎΡ‚Π½Π°, располоТСнного Π² ΠΊΡ€ΠΈΠΎΠ»ΠΈΡ‚ΠΎΠ·ΠΎΠ½Π΅. Π’ дальнСйшСм планируСтся Π½Π° основС Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚ΠΎΠ² диагностики состояния ΠΆΠ΅Π»Π΅Π·Π½ΠΎΠ΄ΠΎΡ€ΠΎΠΆΠ½ΠΎΠ³ΠΎ ΠΏΡƒΡ‚ΠΈ ΠΏΡ€ΠΎΠΈΠ·Π²ΠΎΠ΄ΠΈΡ‚ΡŒ ΠΏΡ€ΠΎΠ³Π½ΠΎΠ·ΠΈΡ€ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΌΠ½ΠΎΠ³ΠΎΠ»Π΅Ρ‚Π½Π΅ΠΌΡ‘Ρ€Π·Π»ΠΎΠ³ΠΎ состояния ΠΎΠ±ΡŠΠ΅ΠΊΡ‚ΠΎΠ² ΠΆΠ΅Π»Π΅Π·Π½ΠΎΠ΄ΠΎΡ€ΠΎΠΆΠ½ΠΎΠΉ инфраструктуры, ΠΏΡ€ΠΎΠΈΠ·Π²ΠΎΠ΄ΠΈΡ‚ΡŒ ΠΊΠ°Ρ‚Π΅Π³ΠΎΡ€ΠΈΡ€ΠΎΠ²Π°Π½ΠΈΠ΅ просадок зСмляного ΠΏΠΎΠ»ΠΎΡ‚Π½Π° ΠΏΠΎ стСпСни опасности ΠΈ Ρ€Π°Π·Ρ€Π°Π±Π°Ρ‚Ρ‹Π²Π°Ρ‚ΡŒ мСроприятия ΠΏΠΎ Π΅Π³ΠΎ стабилизации

    ΠœΠ°Ρ‚Π΅ΠΌΠ°Ρ‚ΠΈΡ‡Π΅ΡΠΊΠΎΠ΅ ΠΌΠΎΠ΄Π΅Π»ΠΈΡ€ΠΎΠ²Π°Π½ΠΈΠ΅ участков ΠΏΠ΅Ρ€Π΅ΠΌΠ΅Π½Π½ΠΎΠΉ Тёсткости ΠΏΠ΅Ρ€Π΅Π΄ искусствСнными сооруТСниями

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    For the English abstract and full text of the article please see the attached PDF-File (English version follows Russian version).ABSTRACT The article deals with the features of the transition zone from the ballast under-sleeper base to the bridge structure with various types of span structures, as well as the sections of the ballastless track, conjugated with the transient zone. An analytical model is proposed for describing the dynamic behavior of a railway track in the form of a transversely isotropic plate with variable rigidity parameters. Examples of the use of the proposed model for calculating the dynamic depression of a roadbed under the influence of a rolling stock with different freight and speed characteristics are given. Keywords: railway, bridge, roadbed, residual deformation, variable rigidity section, track depression, slope, elastic wave, track profile, transversal-isotropic plate.ВСкст Π°Π½Π½ΠΎΡ‚Π°Ρ†ΠΈΠΈ Π½Π° Π°Π½Π³Π». языкС ΠΈ ΠΏΠΎΠ»Π½Ρ‹ΠΉ тСкст ΡΡ‚Π°Ρ‚ΡŒΠΈ Π½Π° Π°Π½Π³Π». языкС находится Π² ΠΏΡ€ΠΈΠ»Π°Π³Π°Π΅ΠΌΠΎΠΌ Ρ„Π°ΠΉΠ»Π΅ ΠŸΠ”Π€ (Π°Π½Π³Π». вСрсия слСдуСт послС русской вСрсии).Π’ ΡΡ‚Π°Ρ‚ΡŒΠ΅ Ρ€Π°ΡΡΠΌΠ°Ρ‚Ρ€ΠΈΠ²Π°ΡŽΡ‚ΡΡ особСнности ΠΏΠ΅Ρ€Π΅Ρ…ΠΎΠ΄Π½ΠΎΠΉ Π·ΠΎΠ½Ρ‹ с балластного подшпального основания Π½Π° мостовоС сооруТСниС с Ρ€Π°Π·Π»ΠΈΡ‡Π½Ρ‹ΠΌΠΈ Ρ‚ΠΈΠΏΠ°ΠΌΠΈ ΠΏΡ€ΠΎΠ»Ρ‘Ρ‚Π½Ρ‹Ρ… строСний, Π° Ρ‚Π°ΠΊΠΆΠ΅ сопряТённыС с Π·ΠΎΠ½ΠΎΠΉ участки бСзбалластного ΠΏΡƒΡ‚ΠΈ. ΠŸΡ€Π΅Π΄Π»ΠΎΠΆΠ΅Π½Π° аналитичСская модСль для описания динамичСского повСдСния ΠΆΠ΅Π»Π΅Π·Π½ΠΎΠ΄ΠΎΡ€ΠΎΠΆΠ½ΠΎΠ³ΠΎ ΠΏΡƒΡ‚ΠΈ Π² Π²ΠΈΠ΄Π΅ Ρ‚Ρ€Π°Π½ΡΠ²Π΅Ρ€ΡΠ°Π»ΡŒΠ½ΠΎ-ΠΈΠ·ΠΎΡ‚Ρ€ΠΎΠΏΠ½ΠΎΠΉ пластины с ΠΏΠ΅Ρ€Π΅ΠΌΠ΅Π½Π½Ρ‹ΠΌΠΈ ΠΏΠ°Ρ€Π°ΠΌΠ΅Ρ‚Ρ€Π°ΠΌΠΈ Тёсткости. ΠŸΡ€ΠΈΠ²Π΅Π΄Π΅Π½Ρ‹ ΠΏΡ€ΠΈΠΌΠ΅Ρ€Ρ‹ использования ΠΏΡ€Π΅Π΄Π»ΠΎΠΆΠ΅Π½Π½ΠΎΠΉ ΠΌΠΎΠ΄Π΅Π»ΠΈ для вычислСния динамичСской осадки зСмляного ΠΏΠΎΠ»ΠΎΡ‚Π½Π° ΠΏΠΎΠ΄ воздСйствиСм ΠΏΠΎΠ΄Π²ΠΈΠΆΠ½ΠΎΠ³ΠΎ состава с Ρ€Π°Π·Π½Ρ‹ΠΌΠΈ Π³Ρ€ΡƒΠ·ΠΎΠ²Ρ‹ΠΌΠΈ ΠΈ скоростными характСристиками

    Deformation analysis based on GNSS measurements in Tashkent region

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    This paper presents the results of the GNSS geodetic network deformation analysis in the Tashkent region, as an example of an urban area, where obtaining reliable information for assessing hazard risk is of great importance. A software package in Delphi language has been developed for the assessment of the datum differences between 2009 and 2011 by implementing the 3D Helmert transformation method. The result revealed that there is significant translation and rotation in the network, while the scale of the network remains almost constant during two years period. The area strain was estimated by the finite element method. Most of the Tashkent region can be considered to be in a high compression (negative dilatation) strain state with maximum value -230cl0-8. On the contrary, remarkable positive dilatation strain is concentrated on the coastline of the Charvak water reservoir, where large strain is about 351.l0-8

    Analytical methods for lignocellulosic biomass structural polysaccharides

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    The use of lignocellulosic biomass has been postulated as a potential pathway toward diminishing global dependence on nonrenewable sources of chemicals and fuels. Before a specific feedstock can be selected for biochemical conversion into biofuels and bio-based chemicals, it must first be characterized to evaluate the chemical composition of the cell walls. Polysaccharides, specifically cellulose and hemicellulose, are often the focal point of these appraisals, since these constituents are the dominant substrates converted into monomeric sugars like glucose and xylose. These monosaccharides can be transformed, using microorganisms like yeast, into substances such as ethanol. Plant species containing abundant polysaccharides are highly desirable, as higher quantities of sugars should translate into larger end-product yields. Given the vast pool of potential feedstocks, qualitative and quantitative analytical methods are needed to assess cell wall polysaccharides. Many of these tools, such as wet chemical and chromatographic techniques, have been ubiquitously used for some time. Shortcomings in these analyses, however, prevent their usage in screening large sample sets for quintessential, high-yield, fuel-producing traits. This chapter briefly summarizes how analytical spectroscopy can lessen some of these limitations and how it has been utilized for polysaccharide analysis
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