The precise tamping mode plays a crucial role in determining the maintenance effects on railway ballasted tracks. Previous studies have focused separately on either the mechanical response or the geometric evolution of tracks, which limits their applicability to onsite maintenance practices. To address this gap, the first optimization study on the precise tamping mode was performed by incorporating both the mechanical and geometric characteristics of the track. A ballast bed–maintaining model was developed using the discrete element method (DEM). On this basis, the influence mechanisms of various maintenance modes on the mechanical behavior of the ballast bed were investigated, revealing the evolution laws of key mechanical indicators, including the lateral resistance, longitudinal resistance, support stiffness, and compactness of the ballast bed. Furthermore, field experiments were conducted, and on the basis of the track geometry inspection data, the improvement effects and deterioration rates of track quality under different maintenance modes were analyzed. The results showed that when the average lifting amount of the working section was less than 20 mm, the tamping + stabilizing (TS) or unilateral double tamping + stabilizing (DTS) mode was recommended; when the average lifting amount exceeds 20 mm, the DTS or tamping + tamping + stabilizing (TTS) mode was preferred.
BarbirOAdamDKopfF, et al.In-situ ballast condition assessment by tamping machine integrated measurement system. IOP Conf Ser Earth Environ Sci2021; 710(1): 12071. https://doi.org/10.1088/1755-1315/710/1/012071
2.
OffenbacherSAntonyBBarbirO, et al.Evaluating the applicability of multi-sensor equipped tamping machines for ballast condition monitoring. Measurement2021; 172: 108881. https://doi.org/10.1016/j.measurement.2020.108881
3.
XiaoHWangXZhangZ, et al. Dynamic stability model and analysis of mechanical properties for railway operations. Comput Times Part Mech. 2023; 10(5): 1205–1219. https://doi.org/10.1007/s40571-023-00560-7
4.
QuJLiuPLongY, et al.Main factors on effect of precise measurement and precise tamping based on bp neural network. Appl Sci2023; 13: 4273. https://doi.org/10.3390/app13074273
5.
ZhangZXiaoHZhuY, et al.Macro–meso mechanical properties of ballast bed during three-sleeper tamping operation. Int J Rail Transp2023; 11(6): 886–911. https://doi.org/10.1080/23248378.2022.2129493
ChiYXiaoHZhangZ, et al.Investigating the impact of tamping on ballast bed in railway turnout areas: insights from discrete element method analysis of energy evolution. Transp Geotech2024; 46: 101243. https://doi.org/10.1016/j.trgeo.2024.101243
8.
ZhouYSchlakeBHansmannF, et al.Particle motion and stress response interacted with machine activity: railroad tamping strategy. Transp Geotech2024; 45: 101188. https://doi.org/10.1016/j.trgeo.2024.101188
9.
AbbasiAZakeriJANorouziE, et al.Field investigation on the effect of the tamping machine and dynamic track stabilizer on changing the rail support modulus. Proc Inst Mech Eng F J Rail Rapid Transit2024; 238(9): 1072–1083. https://doi.org/10.1177/09544097241255718
10.
XiaoHZhangZZhuY, et al.Experimental analysis of ballast bed state in newly constructed railways after tamping and stabilizing operation. Constr Build Mater2023; 362: 129772. https://doi.org/10.1016/j.conbuildmat.2022.129772
11.
PrzybyłowiczMSysynMGerberU, et al.Comparison of the effects and efficiency of vertical and side tamping methods for ballasted railway tracks. Constr Build Mater2022; 314: 125708. https://doi.org/10.1016/j.conbuildmat.2021.125708
12.
ShiSXiaoYXuY, et al.Mechanical properties of rock ballast in combined tamping and stabilizing operations using numerical dynamic domain. Int J Numer Anal Methods GeoMech2025; 49(7): 1838–1852. https://doi.org/10.1002/nag.3961
13.
PereiraPNetoAMottaR, et al.The effects of different types of tamping operations on the degradation of railway track geometry. In: Pombo J, ed. Proceedings of the Sixth International Conference on Railway Technology: Research, Development and Maintenance. Edinburgh: Civil-Comp Press; 2024. (Online volume: CCC 7, Paper 18.3). https://doi.org/10.4203/ccc.7.18.3
QuJ. Research on prediction method of warranty period of track quality based on tamping modes using large tamping machine. J China Railw Soc. 2019; 41(08): 117–122. https://doi.org/10.3969/j.issn.1001-8360.2019.08.015
16.
AfsharTDisfaniMMArulrajahA, et al.Impact of particle shape on breakage of recycled construction and demolition aggregates. Powder Technol2017; 308: 1–12. https://doi.org/10.1016/j.powtec.2016.11.043
17.
WangXYangHXiongX, et al.Ballast particle modeling method based on rblock and Dem numerical simulation of single particle crushing. Constr Build Mater2025; 474: 141052. https://doi.org/10.1016/j.conbuildmat.2025.141052
18.
BianXShiKLiW, et al.Quantification of railway ballast degradation by abrasion testing and computer-aided morphology analysis. J Mater Civ Eng2021; 33(1): 4020411. https://doi.org/10.1061/(ASCE)MT.1943-5533.0003519
19.
JerónimoPResendeRFortunatoE. An assessment of contact and laser-based scanning of rock particles for railway ballast. Transp Geotech2020; 22: 100302. https://doi.org/10.1016/j.trgeo.2019.100302
20.
van der HavenDLHFragkopoulosISElliottJA. A physically consistent discrete element method for arbitrary shapes using volume-interacting level sets. Comput Methods Appl Math2023; 414: 116165. https://doi.org/10.1016/j.cma.2023.116165
21.
ChenJVinodJSIndraratnaB, et al.A discrete element study on the deformation and degradation of coal-fouled ballast. Acta Geotech2022; 17(9): 3977–3993. https://doi.org/10.1007/s11440-022-01453-4
22.
LamHAdeagboMO. An enhanced sequential sensor optimization scheme and its application in the system identification of a rail-sleeper-ballast system. Mech Syst Signal Process2022; 163: 108188. https://doi.org/10.1016/j.ymssp.2021.108188
23.
IndraratnaBNgoTRujikiatkamjornC. Performance of ballast influenced by deformation and degradation: laboratory testing and numerical modeling. Int J GeoMech2020; 20(1): 4019138. https://doi.org/10.1061/(ASCE)GM.1943-5622.0001515
24.
ShiSGaoLHouB, et al.Numerical investigation on multiscale mechanical properties of ballast bed in dynamic stabilization maintenance. Comput Geotech2022; 144: 104649. https://doi.org/10.1016/j.compgeo.2022.104649
25.
WangLZhaoZWangJ, et al.Mechanical characteristics of ballast bed under dynamic stabilization operation based on discrete element and experimental approaches. Shock Vib2021; 2021(1): 6627612. https://doi.org/10.1155/2021/6627612
26.
RichessonSSahimiM. Hertz-mindlin theory of contacting grains and the effective-medium approximation for the permeability of deforming porous media. Geophys Res Lett2019; 46(14): 8039–8045. https://doi.org/10.1029/2019GL083727
27.
ZhaoSEvansTMZhouX. Effects of curvature-related DEM contact model on the macro- and micro-mechanical behaviours of granular soils. Geotechnique. 2018; 68(12): 1085–1098. https://doi.org/10.1680/jgeot.17.P.158
DimitrovováZ. Two-layer model of the railway track: analysis of the critical velocity and instability of two moving proximate masses. Int J Mech Sci2022; 217: 107042. https://doi.org/10.1016/j.ijmecsci.2021.107042
XuCSunHZhouS, et al.Optimizing railway track tamping and geometry fine-tuning allocation using a neural network-based solver. Autom ConStruct2025; 171: 105958. https://doi.org/10.1016/j.autcon.2024.105958
32.
UnsiwilaiSPhusakulkajornWShenC, et al.Enhanced vertical railway track quality index with dynamic responses from moving trains. Heliyon2024; 10(19): e38670. https://doi.org/10.1016/j.heliyon.2024.e38670
33.
RempelosGOgnibeneGLe PenL, et al.Railway track deterioration models: a review of the state of the art. Transp Geotech2024; 49: 101377. https://doi.org/10.1016/j.trgeo.2024.101377
34.
LeeJSYeoIBaeY. A stochastic track maintenance scheduling model based on deep reinforcement learning approaches. Reliab Eng Syst Saf2024; 241: 109709. https://doi.org/10.1016/j.ress.2023.109709
