Optimizing Railway Superstructures: a Multi-Objective Approach To Force Isolation and Cost Efficiency Under Environmental Dynamics

Purpose: This study develops an innovative optimization methodology for railway superstructure design. This methodology aims to achieve the most cost-effective optimal railway superstructure design by minimizing forces transmitted from the rail to the environment while considering environmental factors such as temperature, and by suppressing vibration amplitudes to ensure track stability. The research focuses on achieving Pareto-optimal designs that balance force isolation with cost efficiency for non-ballasted superstructures. Methods: Two common non-ballasted superstructure models were analyzed: a single-layer elastomer pad and a dual-layer configuration. Viscoelastic material behavior, influenced by frequency and temperature, was modeled using an innovative ten-parameter framework integrated with the Generalized Maxwell Model (GMM). Dynamic-mechanical analysis data from twelve elastomer pads informed the model. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) generated Pareto-optimal solutions, with finite element method (FEM) simulations validating dynamic response damping under rail surface irregularities. Results: The optimization yielded Pareto fronts demonstrating effective trade-offs between minimal force transmission and cost. FEM simulations confirmed superior vibration isolation, with significant reductions in dynamic forces transmitted to the track foundation, enhancing environmental protection across operational conditions. Conclusion: The proposed methodology represents a transformative advancement in railway engineering, enabling cost-effective, environmentally sensitive superstructure designs that outperform traditional methods in vibration control and stability.