Impact of heterogeneity in stabilised clay on the settlement of ballasted track

Sammanfattning

Accurately predicting accumulated track settlement and the resulting deteriorationof vertical track geometry is a major challenge in railway infrastructure maintenance.This issue is particularly critical for railways constructed on soft clay soils, whererepeated train loading can lead to excessive and differential settlements, increasedmaintenance demand, reduced ride comfort, and decreased operational reliability.An additional complexity arises from the spatial variability of soil conditions andground improvement measures, which can strongly influence track response andlong-term performance. This thesis investigates the dynamic vehicle–track–soil interaction and long-term settlement behaviour of ballasted railway tracks founded onsoft clay, with particular focus on the influence of spatial variability in lime-cementcolumn (LCC) improved ground. A numerical modelling framework is employedby coupling dynamic finite-element simulations with empirical settlement modelsfor ballast and subgrade layers. Spatial variability in LCC properties is incorporated through random field modelling. Different LCC reinforcement configurationsare evaluated to investigate their influence on track stiffness, stress distribution,and accumulated settlements. In addition, sensitivity analyses are performed toidentify the most influential parameters governing settlement development and trackresponse. The results indicate that spatial variability significantly affects the distribution of track stiffness and the development of differential settlements, while LCCreinforcement effectively reduces long-term settlements and improves overall trackperformance. The developed framework also enables systematic evaluation of mitigation strategies for railway infrastructure subjected to spatially varying soft groundconditions over time. The findings contribute to a better understanding of railwaybehaviour on soft soils and provide a basis for more reliable prediction of long-termtrack performance and maintenance planning.