Transition zones in railways, marked by abrupt changes in foundation stiffness, lead to differential settlements and amplified dynamic loads, accelerating track degradation. This necessitates a method that accurately relates stiffness ratios while accounting for shear interaction within the soil. Traditional models like the Winkler foundation, which neglect soil interaction, often overestimate dynamic responses in such scenarios. This paper presents a closed-form solution for analyzing transition zones, incorporating shear interaction effects modeled through a Visco-elastic Pasternak foundation of abrupt stiffness. The study extends to examine the dynamic responses of a series of axle loads under varying stiffness ratios and speeds, comparing the behavior of Winkler and Pasternak foundations. Results indicate that the Winkler model overestimates the dynamic response by 24%–28% for displacement and 5%–10% for acceleration, approximately. Additionally, the inclusion of the shear layer in the Pasternak model leads to a higher predicted critical velocity compared to the Winkler model. These findings emphasize the substantial impact of foundation resistance as well as resistance due to shear strain on the dynamic performance of the transition zone. Hence, such models can replicate ground behavior more accurately than the Winkler model, offering improved accuracy over traditional Winkler-based solutions. By analyzing various values of the stiffness ratio r, designers and engineers can make informed decisions regarding the selection and placement of materials with tailored stiffness properties in transition zones. This strategic use of materials with varying stiffness enables the design of effective mitigation measures aimed at smoothing out the abrupt changes in structural rigidity.

This study investigates the parametric optimization of multiple-tuned mass dampers (MTMDs) deployed along the length of a tower and monopile structure subjected to stochastic wind and wave forces. The structural components, comprising the tower and monopile, are conceptualized as Euler–Bernoulli beams. The interaction with the soil is modeled through viscoelastic springs distributed along the monopile. The rotor nacelle assembly (RNA) and blades are abstracted into a concentrated mass at the tower's apex. The MTMDs are characterized as spring–mass–dashpot systems strategically positioned at intervals along the tower and monopile. The investigation involves a random vibration analysis under wind and wave loads, employing the Kaimal spectrum for wind and the JONSWAP spectrum for waves. The study focuses on optimizing the TMD parameters using H2 optimization techniques, with the optimization criteria being the minimization of the standard deviation of the tower's top displacement response. Additionally, meta-heuristic algorithms like the genetic algorithm (GA) are utilized to refine the determination of these optimal parameters. A key aspect of the research includes a parametric study that reveals variations in optimal parameters as the number of TMDs is altered, maintaining a consistent total mass across the MTMDs.

This paper introduces a vibration control strategy for bridges that involves the implementation of an inertial amplifier to mitigate train-induced vibrations. The study comprehensively evaluates the effectiveness of two types of vibration absorbers, namely spring-mass resonator (SMR) and inertial amplifiers (IA), using a non-dimensional mechanics-based framework. The study further employs a heuristic search adaptive genetic algorithm (GA) to determine the optimal design parameters for the proposed vibration absorbers. The aim is to minimize the mid-span displacement of the bridge through the optimization process. The theoretical non-dimensional framework and the optimization technique are first validated with existing literature, and further, the efficiency of the optimized IA over the SMR of the same static mass is estimated. The comparative studies elucidate that with a lower mass ratio and higher values of the speed parameter (η) and inter-spatial distance between loads (ϵ), the IA system is more effective in reducing the vibration amplitude due to significant mass amplification. However, for lower values of η and ϵ, the SMR seems effective with a higher mass ratio, consequently resulting in amplified static deflection.

The dynamic interaction between trains, tracks, and bridges is a critical consideration in railway engineering, particularly for designing efficient railway bridges. This interaction holds significant importance, especially in the context of high-speed railways and heavy-haul railways, where optimal bridge design is paramount. Over time, these dynamic loads can lead to fatigue and structural damage, potentially compromising the integrity of the bridge. Vibration control measures help mitigate these dynamic loads, prolonging the lifespan of the bridge and ensuring its structural safety. This paper systematically presents a state-of-the-art review of the vibration control of bridges under the action of high-speed trains in conjunction with bibliometric analysis. Aligning in this direction, an extensive dataset consisting of 2233 research papers was collected from various search engines like Scopus or Web of Knowledge. This provides quantitative insights into research trends, citation patterns, and the impact of scholarly publications within a specific field or discipline. Additionally, a detailed intervention is made based on different vehicle models and methodologies involved in the analysis of vehicle–bridge interaction. This study aids in recognizing emerging topics for research in the domain of vibration control of high-speed railway bridges using different methodologies.

The problem of moving sequential loads over beams is a classic example of vehicle-bridge interaction. Many analytical, numerical, and experiments have been conducted in order to study the dynamic behavior of bridges. However, the computational cost involved in traditional methods is very high. The primary novelty of this paper lies in the creation of a non-dimensional mode-superposition to anticipate the bridge's peak dynamic responses, coupled with the application of ANN-based Multi Input Multi-Output metamodeling. The bridge is idealized as a Euler–Bernoulli beam of uniform cross-section subjected to sequential moving loads commonly known as High Speed Load model-B (HSLM-B). The pearson’s correlation coefficient indicates that the most influential parameter affecting the dynamic response of beams under moving loads is the interspatial distance between loads, i.e., ε followed by speed parameter η. Additionally, the comparison of the best-fitted surrogate models has been conducted to evaluate their robustness and efficiency. The results demonstrate an accuracy level of less than 10 percent, highlighting the high precision and reliability of the surrogate models.