Transition zones in high-speed railways suffer from abrupt stiffness variations that induce irregular dynamic responses and accelerate infrastructure deterioration. This study presents a surrogate-assisted multi-objective optimization framework that combines finite element (FE) simulations, a neural network-based surrogate model, and the NSGA-II algorithm to address this challenge. A validated 3D FE model of prefabricated epoxy asphalt cured track beds (PEACT) was used to generate 341 layout scenarios covering 13 response parameters. These data were used to train a neural network, which served as a static surrogate predictor for evaluating layout performance during the optimization process. The results show that module layout has a limited effect on peak responses but significantly improves smoothness, with three categories of optimal configurations identified. Compared with direct FE-based optimization, the proposed framework achieves substantial computational efficiency and provides data-driven design guidance for PEACT transition zones. This framework exemplifies the potential of hybrid data–simulation approaches to enhance adaptive and efficient railway infrastructure design.

Adequate runway friction capacity during aircraft landing is crucial for flight safety. Accurately evaluating skid resistance under realistic service conditions remains a key challenge for maintaining flight safety. This study proposes a comprehensive skid resistance evaluation method that integrates laboratory testing with finite element simulation. A refined tire-pavement-fluid coupled model was developed, incorporating measured and series-generated worn texture data as key geometric boundary conditions in numerical analysis. The coupled effects of runway texture state, tire kinematics, and water film thickness on skid resistance were systematically investigated. Results suggest that runway macrotexture plays a vital role in maintaining skid resistance, with Stone Mastic Asphalt (SMA) mixtures providing superior skid resistance compared to Asphalt Concrete (AC) mixtures. As runway wear progresses, the combined influence of high speed and thick water films significantly increases the risk of hydroplaning and extends braking distance. This study highlights the significant effects of speed, water film thickness, and texture evolution on runway friction, offering theoretical guidance for material selection and safety evaluation of airport pavements.

The rapid advancement of high-speed railway (HSR) has imposed significantly higher demands on rail tracks' performance and vibration mitigation capabilities, necessitating the design of track systems that can withstand extreme operational conditions while ensuring passenger comfort. Precast epoxy asphalt-cured track (PEACT) has emerged as a promising solution, offering superior mechanical properties and environmental adaptability. In this study, a full-scale finite-element model of PEACT is established and validated. Four dry-mixed rubberised epoxy asphalt mixtures (DREAMs) are incorporated, with their material parameters (e.g., modulus, density, damping) explicitly defined. Several modelling enhancements are implemented beyond conventional response analysis, including: (i) DREAM-grade-specific Rayleigh damping calibration derived from modal analysis, (ii) a stability-first boundary–mesh prescription. These strategies improve modelling fidelity for non-uniform transition zones. The results show that PEACT dynamic responses remain within acceptable ranges, and that DREAMs provide substantial vibration attenuation, contributing an additional ∼40% reduction on top of the fastener system. From a design perspective, these findings provide practical evidence that graded DREAM layouts can effectively control vertical surface displacement to below 0.5 mm under 350 km/h loading, facilitating smoother stiffness transitions, reduced maintenance demand, and more reliable HSR operation.