Analytical forecasting of long-term railway track settlement

Charoenwong, C.; Connolly, David; Woodward, Peter Keith; Galvín, P.; Costa, Pedro Alves

doi:10.1016/j.compgeo.2021.104601

Cited by 36 publications

(9 citation statements)

References 55 publications

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“…Previous studies have shown that most land subsidence events are caused by fluctuations in the water table [43][44][45][46][47], railway loading [48][49][50], geological faults [51][52][53], urban sprawl [54][55][56], and the lithology of the ground [57][58][59]. Chen et al [60] used InSAR to monitor the spatial and temporal evolution of surface subsidence in Lanzhou New District from 2003 to 2016.…”

Section: Introductionmentioning

confidence: 99%

Land Subsidence Monitoring and Building Risk Assessment Using InSAR and Machine Learning in a Loess Plateau City—A Case Study of Lanzhou, China

Zhang

et al. 2023

Remote Sensing

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As a representative city located in the Loess Plateau region of China, Lanzhou is affected by various environmental and engineering factors, such as precipitation, earthquake subsidence, and building construction, which all lead to frequent geological disasters. Obtaining information on land subsidence over a long time series helps us grasp the patterns of change in various types of ground hazard. In this paper, we present the results of using Interferometric Synthetic Aperture Radar (InSAR) to monitor land subsidence in the main urban area of Lanzhou from 26 October 2014 to 12 December 2021. The main influential factors leading to subsidence were analyzed and combined via machine learning simulation to assess the land subsidence risk grade distribution of a building unit. The results show that the annual average deformation rate in Lanzhou ranged from −18.74 to 12.78 mm/yr. Linear subsidence dominated most subsidence areas in Lanzhou during the monitoring period. The subsidence areas were mainly distributed along the Yellow River, the railway, and villages and towns on the edges of urban areas. The main areas where subsidence occurred were the eastern part of Chengguan District, the railway line in Anning District, and the southern parts of Xigu District and Qilihe urban area, accounting for 38.8, 43.5, 32.5, and 51.8% of the area of their respective administrative districts, respectively. The random forest model analysis results show that the factors influencing surface subsidence in Lanzhou were, in order of importance, precipitation, the distribution of faults, the lithology of strata, high-rise buildings, and the distance to the river and railway. Lanzhou experienced excessive groundwater drainage in some areas from 2015 to 2017, with a 1 m drop in groundwater and 14.61 mm surface subsidence in the most critical areas. At the same time, extensive subsidence occurred in areas with highly compressible loess ground and most railway sections, reaching a maximum of −11.68 mm/yr. More than half of the super-tall building areas also showed settlement funnels. The area at a very high risk of future subsidence in Lanzhou covers 22.02 km2, while the high-subsidence-risk area covers 54.47 km2. The areas at greatest risk of future subsidence are Chengguan District and Qilihe District. The city contains a total of 51,163 buildings in the very high-risk area, including about 44.57% of brick-and-timber houses, 51.36% of old housing, and 52.78% of super-tall buildings, which are at especially high risk of subsidence, threatening the lives and properties of the population. The deformation results reveal poor building safety in Lanzhou, providing an essential basis for future urban development and construction.

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Section: Introductionmentioning

confidence: 99%

Land Subsidence Monitoring and Building Risk Assessment Using InSAR and Machine Learning in a Loess Plateau City—A Case Study of Lanzhou, China

Zhang

et al. 2023

Remote Sensing

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“…The purpose of these was to understand how a track behaves, so predictions about its quality can be made. In more recent research, a numerical model was developed, which has the ability to calculate differential track settlement [6]. It is based on Finite Element Method with perfectly matched layers (FEM-PML) and solved across two domains (frequency-wavenumber and time-space).…”

Section: Introductionmentioning

confidence: 99%

Big-data driven assessment of railway track and maintenance efficiency using Artificial Neural Networks

Popov

Bold²,

Chai³

et al. 2022

Construction and Building Materials

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“…Differential track stiffness across a transition can cause problems that are exasperated by high train speeds and axle loads. Further, the quality of track material, particularly the compaction of ballast and subgrade [2,4,12,13], including the presence of singular rail and wheel surface defect due to the structural discontinuity can generate high-level of track-ground vibration [14][15][16][17] which play important role in transition zone track degradation.…”

Section: Introductionmentioning

confidence: 99%

The effect of soil improvement and auxiliary rails at railway track transition zones

Chumyen

Connolly

Woodward

et al. 2022

Soil Dynamics and Earthquake Engineering

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Analytical forecasting of long-term railway track settlement

Cited by 36 publications

References 55 publications

Land Subsidence Monitoring and Building Risk Assessment Using InSAR and Machine Learning in a Loess Plateau City—A Case Study of Lanzhou, China

Land Subsidence Monitoring and Building Risk Assessment Using InSAR and Machine Learning in a Loess Plateau City—A Case Study of Lanzhou, China

Big-data driven assessment of railway track and maintenance efficiency using Artificial Neural Networks

The effect of soil improvement and auxiliary rails at railway track transition zones

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