Evolution and Smoothness Evaluation of the Precise Adjustment of Track Vertical Alignment on Kilometer-Scale Railway Bridge Under Multiple Influences

Long-span railway bridges are highly sensitive to external influences such as temperature variation and train loading, which can lead to significant deformation of the track vertical alignment and deterioration of running performance. To address this challenge, this study investigates the evolution and smoothness evaluation of precisely adjusted vertical alignment on a kilometer-scale suspension bridge under multiple influences. A Fourier series–based fine-tuning method is developed, optimized using a hybrid genetic algorithm and Gaussian process regression, to generate continuous adjustment schemes that eliminate vehicle-sensitive wavelength components while ensuring ballast adjustment feasibility. A kilometer-scale railway–highway suspension bridge is adopted to analyze alignment evolution under thermal effects and train-induced deformation. Smoothness is evaluated through chord offset indices, vehicle dynamic responses, and an energy-based criterion considering vibration energy and viscoelastic drag. The results reveal that the fine-tuned alignment maintains excellent smoothness and dynamic performance under combined influences, with chord offsets and carbody accelerations remaining within regulatory limits. Energy-based evaluation further confirms that precise adjustment effectively reduces vibration energy dissipation and running resistance, thereby enhancing long-term stability. The proposed framework provides both a theoretical basis and an engineering approach for the design and maintenance of kilometer-scale railway bridges, ensuring track geometry stability and smooth operation under complex service conditions.