International Journal of Structural Stability and Dynamics

In this study, a procedure for constructing pavement roughness in a three-dimensional bridge subjected to a vehicle using Abaqus is proposed for numerical simulation of vehicle–bridge interaction problems. As the plug-in RufGen, a graphical user interface in Abaqus/CAE for surface interaction, creates a hexahedron with a rough surface and attaches it to the bridge deck, the resulting weight of a bridge model can be much larger than the original one. In the vehicle scanning method, it is essential to keep a finite element bridge model with an unchanged weight after imposing a rough surface on it in consideration of the roughness effect for bridge health monitoring. As such, the proposed procedure is aimed at resolving this issue, with the following two highlights: First, the Toeplitz matrix is introduced to generate a rough surface in three dimensions to ensure the randomness of roughness in the two perpendicular directions under a given roughness profile. Second, a shell-type rough pavement is established for directly using as the bridge pavement or attaching to the bridge deck, which minimizes weight increment in the bridge.

The steel–concrete hybrid tower (SCHT) has been popularized as the supporting structure for onshore wind turbines to meet the increasing hub height and rotor diameter. The internal force of the cross-section, which is the prerequisite of the structural design, is based on the load analysis. However, the nonignorable effect of the second-order gravity makes analyzing the external load of the supporting tower a coupled problem, especially for the SCHT, which is higher than a conventional steel tubular tower. Besides, the aerodynamic load of the wind turbine, the main excitation acting on the supporting tower, is usually computed by numerical methods and lack of validation. To optimize the analysis method for the internal force of the SCHT, one feasible approach is evaluating the external excitation directly. Based on the theory of the Kalman filter, this study develops a dynamic inverse method of the SCHT. It aims to estimate the horizontal thrust of the wind turbine and provide an overall evaluation of the dynamic responses of the SCHT by using the measured dynamic responses at the limited point on the SCHT. A rigorous numerical simulation based on ABAQUS is conducted to generate the dynamic responses of the SCHT, which are used to validate the effectiveness of the proposed method. It also investigated the influence of observation type, number of measuring points, and noise level on the estimation accuracy.

In this study, a bridge damage identification method based on the Long Short-Term Memory (LSTM) network and the contact point response is proposed, which utilizes the time behavior of the contact point response. The contact point response serving as the input to the LSTM network is obtained by calculating the vehicle-bridge interaction motion equation. A new bridge damage indicator titled the Euclidean Distance Damage Index (EDDI) is developed based on the difference between the actual and predicted values of the contact point response. Under ideal road surface conditions, the EDDI calculated in the healthy state of the bridge remains below 0.16, while exceeding 1.0 in the damaged state. When road roughness is considered, the EDDI is calculated to be less than 0.12 in the healthy state of the bridge and higher than 0.65 in the damaged state. The results show that the EDDI is more effective in distinguishing between the damaged and the healthy states of the bridge. Meanwhile, road roughness has a negative effect on the damage sensitivity of EDDI.

In the field of structural health monitoring, vibration-based damage identification remains a formidable challenge. Key to this challenge is the establishment of a reliable association between observed vibration characteristics and the actual state of structural damage (e.g. stiffness reduction). This association not only accurately indicates the presence of damage, but also the location and severity of the damage. To solve this complex pattern identification problem, a large number of approaches, including deep learning, have emerged in recent years. In this paper, we propose a new structural damage identification method that utilizes the vibration information of the structure and a convolutional neural network based on Alex NET improvement. The method consists of calculating the acceleration response power spectral density of damaged and undamaged structures under impact loading separately, and then making a difference between the two power spectral data, and subsequently introducing these power spectral difference data into the convolutional neural network for training. The use of power spectral density analysis as a preprocessing step converts the time-domain signals into frequency-domain signals, and this conversion allows the convolutional neural network to capture and learn from the specific frequency characteristics of the data, thus facilitating the learning process of the neural network model. In this paper, the effectiveness of the method is critically evaluated through numerical simulation and experimental validation, and 3% and 5% noise are added to the numerical study to test the robustness of the method. During the convolution neural network training process, the optimal training mean squared error (MSE) is $5\times 10^{-6}$ in the case of no noise addition; the optimal training MSE is $1.3\times 10^{-5}$ in the case of noise addition. Both the results of simulations and experiments confirm the high accuracy and good robustness of the method in localizing structural damage.

In this study, we explore the complex vibration problem of a Rayleigh beam under axial loads, positioned over a variable Winkler foundation with elastic properties changing along its length. The investigation delves into how factors like rotational inertia, axial forces, and foundation properties affect the beam’s dimensionless eigenvalues and eigenmodes. Employing the Differential Transform Method (DTM), we tackle the governing equations across various foundation conditions, including constant, linear, and parabolic variations. The results align well with the previous research, affirming their precision between scenarios considering or neglecting the elastic foundation’s effects. Notably, we emphasize the influence of linear spring stiffness on vibration characteristics, overshadowing rotary spring stiffness. Thus, the important novelty of the study lies in its comprehensive investigation into the vibration dynamics of a Rayleigh beam under axial loads on a variable Winkler foundation with elastic properties varying along its length. The findings illuminate a previously unexplored dynamic: the intricate interplay between linear and rotary spring stiffness, showcasing the dominant impact of linear stiffness on vibration characteristics. Of significant note is the profound effect of axial forces on fundamental eigenvalues, underscoring their critical role in structural behavior. These developments possess substantial practical implications, providing invaluable guidance for the accurate modeling of beams supported by elastic foundation, thereby enhancing structural design and performance.

To mitigate the metro-induced vertical vibration of the indoor substation structure, this study proposes a gas-spring quasi-zero stiffness air damping isolator (AD-QZSI) with excellent low dynamic stiffness and high-static stiffness characteristics. The working principle and mechanical properties of the AD-QZSI are introduced and studied through theoretical and numerical methods. A model for substation considering soil–structure–equipment interaction is established using the software ABAQUS, its accuracy is validated based on a series of measured data from actual projects, and the AD-QZSI’s simulation method and parameter design method are described in detail. The air damper’s stiffness $k_a$ is integrated into the isolator’s mechanical model, theoretically and numerically achieving an accurate simulation of AD-QZSI’s nonlinear mechanical properties. The numerical results have an error of less than 5% with the measured data, indicating that the model is able to better capture the actual structure’s dynamic characteristics and is reasonable to be employed for subsequent analysis. Numerical results show that AD-QZSI can significantly reduce the structural vertical vibration, and its control effect is better in the whole frequency band, in particular, the effect is also visible in the low-frequency band, indicating that its vibration isolation frequency band is wider than that of traditional QZS isolator. With the vibration source distance increasing, the control effect of AD-QZSI presents a tendency to decrease and then level off, and its vibration isolation gain is weakened by the continuous increase of the damping ratio greater than 0.01. Moreover, the equipment’s dynamic amplification factor of the isolated structure decreases significantly. Finally, the proposed AD-QZSI can obtain ideal quasi-zero stiffness characteristics by adjusting the air pressure, and the adopted air damper belongs to the green low-carbon components, featuring great practical value and application prospects.

The hub motor driven vehicle (HMDV), due to the increase in unsprung mass and the influence of uneven electromagnetic radial forces from road excitations, results in the deterioration of body acceleration, significantly impacting ride comfort. This seriously impacts ride comfort. However, the inerter proves effective in reducing low-frequency vibrations in body acceleration, and skyhook control significantly enhances ride comfort. To fully exploit the benefits of the inerter, comprehensively elevate skyhook control performance, and effectively counteract the worsening of body acceleration caused by the hub motor, this study integrates skyhook control with an inertial suspension system. This integration markedly improves the suspension’s low-frequency isolation performance, thereby substantially enhancing vehicle ride comfort. Firstly, establish a quarter-vehicle dynamics suspension model based on the Switched Reluctance Motor (SRM) driven wheel motor system. Subsequently, confirm the constraints of two different generalized skyhook inertial suspension structures separately and optimize their parameters to determine the most suitable parameters for each structure. Finally, analyze the impact of these two suspension types on body acceleration to derive the optimal structure. Simulation results demonstrate that, compared to traditional suspensions, the optimized generalized skyhook inertial suspensions reduce the Root Mean Square (RMS) values of body acceleration and suspension working space by 18.8% and 22%, respectively. This reduction is particularly significant in the 0$\sim$2 Hz frequency range, effectively enhancing ride comfort. It also adequately validates the effective utilization of the advantages of inertial suspensions and skyhook control.

The dynamic performance of railway vehicles is significantly impacted by sudden-changed aerodynamic loads. To investigate the dynamic performance of high-speed trains (HSTs) under tunnel and crosswind environments considering the car-body flexibility, an intensive study is conducted. First, a train–track interaction dynamic model with a flexible car body is established in SIMPACK for dynamic response analysis. Concurrently, an aerodynamic model for calculating the distribution of aerodynamic loads is found in FLUENT to determine forces and moments applied to each part of the car body. Then, the two models are coupled utilizing a co-simulation method developed based on the User Data Protocol (UDP). Finally, a case study is carried out, involving a train passing through tunnels subjected to crosswinds. The results reveal that the distribution of aerodynamic loads on the car-body affected by crosswind is time-variant and non-even. Interestingly, the dynamic simulation results are almost unaffected by the method used to allocate the loads on the car body. Variations in the aerodynamic loads affected by crosswinds lead to flexible first-order diamond mode vibration of the car body at around 8.5 Hz when exiting the tunnel. As the crosswind speed continues to increase, vibrations at frequencies of 18.2 Hz and 24.2 Hz will be enhanced, corresponding to the bending mode and combined mode of the car body. However, similar flexible vibrations are insignificant when the vehicle enters the tunnel. In addition, the vertical wheel–rail interaction obtained by the dynamic model with a rigid car body is slightly greater than that with a flexible car body.

The elastic medium can usually reduce the vibration of supporting structure, and the impact of the interaction on the vibration characteristics of the structure is similar to the characteristic of nonlinear energy sink. At present, the dynamic research of beam on elastic foundation considering soil motion has been paid more and more attention. According to the modified Winkler model, the finite-depth elastic medium can be considered to the nonlinear energy sink mass, and the vibration energy dissipation capacity and parameter optimization of the elastic medium supporting finite-length beam under half sine pulse are studied. The Galerkin truncation is applied to the discretization of the governing equations. The numerical solution of the beam coupling system with simple support on the elastic medium is obtained by applying the fourth-order Runge–Kutta method. Based on this, the input energy ratio of the elastic medium dissipation is investigated. Furthermore, through the analysis and optimization of targeted energy transfer and dissipation, the dissipation effect of the finite range elastic medium on the vibration energy of its supporting beam is revealed, and the optimal parameter range of the elastic medium is proved. The results show that after adjusting the elastic medium parameters by technical means, the nonlinear energy sink can absorb most of the vibration energy of the beam quickly and effectively, and the optimal energy dissipation ratio can reach 95.16$\%$. The quantitative evaluation of the energy dissipation in elastic medium within soil–structure interaction effect is realized.

Previous studies have shown that sampling processing and identification methods for neural networks damage identification in multi-channel and multi-source structural dynamic response data exhibit diversity, and the robustness and generalization issues of the model have not been effectively solved. This paper proposes a sample preprocessing technique and mixed training method suitable for multi-channel and multi-source dynamic response data to optimize the current structural damage identification methods based on neural networks. Through multi-dimensional discrete autocorrelation processing and Fourier transform, the preprocessed datasets from multiple sensors with multi-channel can access multi-channel CNN. Furthermore, the measured datasets from the actual structures are mixed with numerical simulation datasets before being used for neural network model training to address the model calibration and the imbalanced sample size under each classification label. The results of extensive model experiments and finite element verification of specimens show that the method performs outstandingly in detecting and identifying damage to simply supported beam structures using multi-channel CNN. The neural network model trained with preprocessed samples exhibits excellent robustness. The model using the mixed training method still performs well in identifying the accuracy of damage location and degree in simply supported beam structures after changing beam section and initial excitation. The method has certain generalization ability in detecting unknown damage in simply supported beam structures.

Disasters such as ice and wind can pose a serious threat to the normal operation of power transmission lines. Wind-induced fatigue damage can further reduce the load-carrying capacity of transmission towers, increasing their fragility to ice and wind disasters. To assess the comprehensive capability of transmission towers to resist extreme ice and wind disasters, this paper proposes a failure probability evaluation framework for transmission towers considering wind-induced fatigue damage under the coupled effect of ice and wind. Taking a certain transmission line in Hunan as an example, the failure probability caused by ice and wind disasters is calculated for different years of service. First, based on the historical meteorological data in three cities in Hunan, a joint probability model of wind speed and wind direction considering their correlation is established using the copula function. Then, based on this probability model, the wind-induced fatigue damage of transmission towers is calculated using the Miner linear damage theory and the S-N curve. Subsequently, the fragility of the tower under the coupled load of ice and wind is calculated for different years of service. Finally, by combining the structural fragility function with the joint probability distribution model of ice thickness and wind speed, the collapse probability of the transmission tower under the action of ice and wind disasters is calculated. The results indicate that the influence of wind-induced fatigue damage cannot be ignored when transmission towers encounter ice and wind disasters. With increasing service time, the ability of transmission towers to resist ice and wind disasters gradually decreases, and the failure probability also increases under extreme ice and wind conditions.

The acceleration of the vehicle body is closely related to the wavelength components of the track, making the design of track alignments crucial for enhancing the comfort of high-speed rail travel. However, research on the effects of long-wavelength deformations on alignment design and their impact on vehicle dynamics remains limited, and existing methodologies fail to effectively calculate the wavelengths of traditional alignments, which consist of linear segments and vertical curves. This study introduces a novel approach for computing long-wavelength track alignments and vehicle response wavelengths using signal decomposition and the Hilbert transform. A comprehensive program is developed to analyze the relationship between alignment and vehicle response wavelengths through correlation coefficients and coherence functions. The results show that traditional alignments have predominant wavelengths exceeding 1 000m, which exhibit negligible correlation with vehicle response due to the dominant influence of alignment curvature. In contrast, Fourier series-designed alignments demonstrate a significant correlation with vehicle response, particularly under high-speed conditions. Quantitatively, vehicle response wavelengths are found to range from 1000 m to 5000 m, with Fourier series alignments effectively reducing dynamic impacts. These findings highlight the potential of the proposed approach in optimizing track design for challenging railway sections, providing valuable guidance for improving high-speed railway operations.