In order to enhance the wind resistance for transmission towers, this study proposes an active control technique using prestressed cables and develops a reinforcement evaluation framework grounded in dynamic stability theory.A typical transmission tower-line coupled system was modeled, and a finite element model incorporating active cable control was constructed to compare the dynamic responses of the tower before and after reinforcement under the design wind speed.The failure modes and ultimate wind speeds of the tower-line system before and after reinforcement were subsequently analyzed using dynamic stability theory, followed by a parametric study on the influence of the cable tension threshold.The results show that the ultimate wind speed of the unreinforced tower is 25.4 m/s; with a cable tension threshold of 50.000 kN, it increases to 28.0 m/s, and further to 30.0 m/s when the threshold reaches 100.000 kN.The active cables effectively reduce the static component of axial force responses in the diagonal members and restrain out-of-plane displacements, although increasing the axial force amplitude in the compressive leg members.The analysis indicates that the technique does not alter the failure mode of the tower but delays structural failure by reducing the axial force time histories of critical members.The enhancement of ultimate wind speed increases with the increase of the cable tension threshold, which should be tuned in accordance with the predicted wind speed.

In order to accurately predict transmission line galloping under extreme weather, this study proposes an integrated analytical method.First, a long short-term memory network (LSTM) optimized with a Bayesian algorithm was constructed for ice thickness prediction.Second, an improved differential evolution algorithm was used to determine parameters for wind and ice load distributions.The joint probability characteristics of these loads were then modeled by selecting an optimal Copula function.Finally, galloping probability was calculated.A case analysis based on historical data from the Xinjiang power grid was conducted.The results show that this integrated prediction model is effective and accurate.

In multi-sound event detection tasks, highly overlapping sound events and a scarcity of strongly labeled datasets are common challenges.Additionally, the varying durations of different sound events and the structural randomness of audio signal spectral features in noisy environments make it difficult for single-scale features to capture all information about multi-sound events.This results in poor detection performance for models with fixed receptive fields.To address this challenge, this study proposes the adaptive mean median-empirical mode decomposition-multichannel attention capsule network-bidirectional gated recurrent unit (ME-McAttCapsNet-BiGRU) method for weakly labeled multi-sound event detection.The AMM-EMD algorithm was employed for audio signal denoising, while multi-scale parallel fusion convolutions extracted multi-scale fusion features.To fully leverage this multi-scale fusion feature information, a multi-channel heterogeneous convolutional capsule structure was constructed, dynamically assigning the most discriminative frequencybands to each event.A soft attention dynamic routing algorithm was employed within the multi-channel attention capsule network to prioritize more significant frame information.Furthermore, a bidirectional gated recurrent unit (BiGRU) was utilized to fully learn temporal contextual information and optimize the correlation of label sequences, thereby enhancing the model’s temporal localization capability.The proposed method was evaluated against baseline approaches on weakly labeled datasets from DCASE 2017 Task 4 and DCASE 2022 Task 4.The results show that compared with the baseline model capsule network, the proposed method increases the Event-based F1 scores by 23.7% and 24.2% respectively on the audio annotation task, and the Event-based F1 scores by 26.6% and 18.1% respectively on the event detection task.PSDS1 increases by 0.261 and 0.246 respectively, and PSDS2 increases by 0.257 and 0.243 respectively.The proposed architecture enhances the performance of weakly labeled multi-sound event detection.

Installing vibration isolation devices on equipment is one of the important means to ensure efficient operation of the equipment. Inspired by the unique structure of cat claws, and combining thin-walled curved beam structures with elastic buckling characteristics, a tunable load low-frequency vibration isolator with high-static stiffness and low-dynamic stiffness (HSLDS) properties is proposed. This vibration isolator consists of thin-walled arch structural units (simulating cat toes) and annular structural units (simulating metatarsal pads). The elliptic integral method is used to analyze the static large deformation characteristics of thin-walled arches and rings, and their nonlinear mechanical characteristics are verified through experiments and finite element models. The influence of geometric parameters of combined isolator units on its load-bearing capacity was discussed. By rationally designing the structural parameters of the unit, quasi-zero-stiffness isolation structure units that meet different load requirements can be obtained. The results show that when the arc length ratio of the ring to the arch is approximately 1.2, the structure exhibits quasi-zero-stiffness characteristics; within the range of installation angle variations considered in the calculations, the maximum load at the minimum stiffness point can reach three times the minimum load. Experimental results on vibration transmission characteristics demonstrate that the lowest isolation frequency of this structure can be reduced to around 1.6 Hz. These findings can provide reference for the design of low-frequency isolation mechanisms for thin-walled curved beams.

To address the intense attitude oscillations and resultant structural fatigue of floating offshore wind turbines in deep-sea environments, this paper investigates a thruster-assisted three-column platform and establishes a control-oriented dynamic model. A dual-channel linear active disturbance rejection control strategy is proposed for heave and pitch motion control. By constructing an extended state observer, environmental loads, inter-channel couplings, and model uncertainties are lumped as a “total disturbance,” which is subsequently estimated and compensated online via a feedforward mechanism. This approach significantly reduces the controller’s reliance on precise hydrodynamic parameters. Numerical simulations demonstrate that, compared to conventional proportional-integral-derivative control, the proposed LADRC scheme significantly suppresses heave and pitch responses. Furthermore, it exhibits superior robustness across various sea states. This study provides a critical theoretical basis and engineering reference for the attitude stabilization of thruster-assisted FOWTs.

An acoustic metamaterial was constructed by arranging multiple resonators in a multi-layer concentric grid configuration. The sound insulation characteristics and mechanisms of this structure were investigated through numerical simulations and experimental validation, with a focus on the influence of partition structural parameters, cavity dimensions, ventilation slit covers, and unit assembly methods on its acoustic performance. The results indicate that the structure can generate three sound insulation peaks and a broad, flat insulation band over a wide frequency range, corresponding to the resonance mechanisms of the annular resonant cavities and the ventilation slits, respectively. By adjusting the partition thickness, the diameter and number of through-holes on the partitions, the depth and width of the back cavity, and by embedding porous materials within the back cavity, the frequency range and amplitude of the insulation peaks and the flat band can be effectively modulated. Appropriately adding perforated covers to the ventilation slits or connecting individual units in series can both enhance the amplitude of the peaks and valleys in the transmission loss curve and introduce new broadband resonance peaks in the mid-to-high frequency range. Experimental samples designed for dominant environmental noise frequency bands validated the structure's broadband ventilation-insulation performance in the frequency band under test.

As a core transmission component in robotic joints, the harmonic reducer presents significant challenges in mechanical analysis due to the nonlinear fit between the flexspline and the flexible bearing. Under the small-deformation assumption, the flexspline exhibits linear behavior. Based on the principle of linear superposition, this study decomposes the deformation of the flexspline into axisymmetric and non-axisymmetric components, and establishes corresponding reaction force models for each deformation component. This approach substantially simplifies the modeling complexity and leads to the derivation of a mechanical model for the flexspline under preload conditions. Subsequently, parametric modeling was carried out using ANSYS finite element software, and simulation validation was performed with a focus on three key aspects: the deformation displacement of the flexspline, the contact pressure between the flexspline and the wave generator, and the radial shear force between the gear ring and the flexspline cup. A comparison between the theoretical model and simulation results shows good agreement, with the maximum error for all three indicators remaining below 5%. This research provides an accurate theoretical analysis tool for the structural design and performance enhancement of top-hat harmonic reducers.

To address the low signal-to-noise ratio, difficult defect identification, and lack of comprehensive evaluation in 304 stainless steel weld inspection, a combined method based on phased array ultrasonic testing (PAUT) and time of flight diffraction (TOFD) was proposed. Four types of defects were designed, including lack of penetration, cracks, porosity, and slag inclusions. A comprehensive evaluation index (CI) and fatigue life index (Nf) were established based on particle swarm optimization and Paris law. All defects were effectively detected, with average errors of 0.35 mm, 0.48 mm, 0.49 mm, and 0.31 mm, and absolute errors less than 0.5 mm. The CI values of PAUT, TOFD, and PAUT+TOFD are 1.5×10⁻², 4.3×10⁻², and 5×10⁻³, while the corresponding Nf values are 2.1×10⁶, 1.9×10⁶, and 2.3×10⁶, respectively. The combined method shows the best performance with the smallest CI and largest Nf. This approach enables quantitative evaluation of defect impact and fatigue life, providing support for weld assessment.

A Bézier curve-based parametric optimization method was developed to mitigate impact, vibration, and noise in rolling linear guideway return units during high-speed reversal. A transient collision mechanics model was established using Hertzian contact theory and differential geometry. The control points of a ninth-order Bézier curve served as design variables for a nonlinear optimization problem, solved with a multi-start sequential quadratic programming (SQP) method incorporating the BFGS update. A parametric modeling platform was created to rapidly generate return unit geometry and ensure consistent ball arrangement. Validation via numerical analysis, ADAMS simulation, and bench tests demonstrated that the optimized design significantly reduces transient peak contact force and vibration amplitude, enhancing reversal smoothness and positioning stability.

Measurement-while-drilling lithology measurement is a key link in building a transparent geological model of coal mines. To achieve real-time lithology identification during drilling, a method based on multi-domain features and GWO-SVM is proposed by utilizing the triaxial vibration response near the drill bit. Firstly, the triaxial vibration acceleration signals are decomposed by continuous variational mode decomposition (SVMD), and the IMFs rich in features are selected based on the multi-scale sample entropy criterion and the signals are reconstructed. Then, a statistical feature extraction module for time domain, frequency domain and time-frequency domain features and a recurrence plot (RP) image feature extraction module are constructed. Finally, the statistical features and image features are concatenated into a multi-domain fusion feature vector and input into the SVM classification model optimized by GWO with the Pareto mechanism to achieve lithology identification. To verify the effectiveness of the method, a logging-while-drilling lithology measurement platform was built, a near-bit measurement subassembly was designed, seven types of rock samples made of similar materials were prepared, and a lithology identification dataset was constructed. Through ablation experiments, comparisons of different signal reconstruction methods and different classification models, it is shown that the proposed method achieves an average recognition accuracy of 98.48% for seven types of lithologies under four working conditions. Its signal reconstruction effect is superior to that of variational mode decomposition, empirical mode decomposition and complete ensemble empirical mode decomposition with adaptive noise methods, and its classification performance is significantly better than that of typical models such as BP neural network, K-nearest neighbor algorithm,Decision Tree and MobileViT,It can achieve efficient and accurate lithology identification during drilling.

To study the durability of concrete structures in cold regions, dynamic Brazilian disk splitting tests were conducted on carbonated, freeze-thawed (temperature difference of 40℃ and 100℃) and their coupled damage concrete specimens with different cycle numbers using a Hopkinson pressure bar and a high-speed camera. Based on the test results, the dynamic splitting tensile strength, energy dissipation law, failure mode, strain field distribution and strain rate effect of the five types of damaged concrete specimens were analyzed, revealing the dynamic damage evolution law of concrete under multi-field coupling. The test results show that the dynamic splitting tensile strength and energy dissipation capacity of the specimens significantly degrade with the increase of damage cycles, with the maximum strength reduction reaching 52.6%; the energy dissipation ratio of the specimens is between 0.2 and 0.6 and is negatively correlated with the number of damage cycles. With the accumulation of damage, the crack width and number of the specimens increase, and the specimens change from typical brittle splitting failure to crushing failure. Concrete specimens with larger damage variables still exhibit significant strain rate effects, but the strain rate sensitivity decreases as the degree of damage deepens. The coupling effect has a greater impact on concrete than a single factor, and specimens with a temperature difference of 100℃ are more severely damaged than those with a temperature difference of 40℃.

To address the difficulty in quantitatively characterizing the internal flow state and energy transfer characteristics of a hydraulic torque converter, this paper proposed a kinetic stiffness characterization and visualization method for the hydraulic torque converter system under variable operating conditions. Based on the energy transmission and conversion mechanisms of the system, sub-models of pump flow stiffness and turbine speed stiffness were first established. These models were used to reveal the relationship between internal parameter variations and external characteristic responses. Then, a visualization method for the kinetic stiffness of the hydraulic torque converter system was proposed. In this method, the speed and flow characteristics of the system were mapped into Lissajous figures and kinetic stiffness circles in a Cartesian coordinate system. Finally, experiments on the kinetic stiffness of the hydraulic torque converter system were carried out. The results show that under steady-state external characteristic conditions, the kinetic stiffness angle and circle area of the pump remain basically stable. In contrast, the kinetic stiffness angle and circle area of the turbine decrease significantly with increasing rotational speed. This indicates that the system gradually changes from a flexible state to a rigid state, and the energy loss is reduced. Under fixed speed ratio conditions, when the speed ratio is 0.6, the kinetic stiffness circle area of the pump increases with turbine speed, whereas that of the turbine decreases. Meanwhile, the annular area between the two circles is reduced by about 46.76%, and the corresponding transmission efficiency increases from 18.53% to 62.32%. When the speed ratio is 0.2, the annular area of the kinetic stiffness circle is obviously larger than that under the speed ratio of 0.6. This suggests stronger internal circulation energy dissipation and lower transmission efficiency, which is consistent with the low-efficiency operating characteristic at this speed ratio. Under variable speed ratio conditions, the kinetic stiffness of the pump remains relatively stable, while the kinetic stiffness angle and circle area of the turbine decrease as the speed ratio increases. The system therefore gradually shifts from a flexible state to a rigid state. The visualization results of kinetic stiffness are in good agreement with the variation trend of the efficiency curve. The kinetic stiffness Lissajous diagram can characterize the dynamic coupling and operational stability of the hydraulic torque converter system. The kinetic stiffness circle can reflect the energy transmission efficiency and the transition characteristics between flexible and rigid states. Therefore, the proposed method provides an effective approach for visual monitoring of the operating state of hydromechanical transmission systems.

To investigate the effects of vertical pressure on the frictional sliding behavior of aged laminated rubber bearings, horizontal cyclic loading tests were conducted on bridge rubber bearing pads with 10 years of service history. These were compared with new bearing pads. The tests analyzed the influence of axial compression and service duration on the evolution of bearing friction and sliding, energy dissipation capacity, equivalent stiffness, and friction coefficient. Results indicate: Increasing vertical load significantly enhances both the initial sliding load and energy dissipation capacity. At 400% equivalent shear strain, the cumulative energy dissipation under 8 MPa vertical load reaches 125% of that under 4 MPa conditions. The friction coefficient of aged bearings decreased compared to new ones. After 10 years of service under identical vertical pressure, the static friction coefficient of aged bearings decreased by approximately 30% on average compared to new bearings, while the sliding friction coefficient decreased by about 25%. The equivalent stiffness of aged bearings significantly degrades, with the rate of stiffness decline accelerating as displacement increases. A simplified bilinear mechanical model for horizontal shear in rubber bearings was established, accounting for service time and the coupled effects of vertical axial compression. This model effectively describes the nonlinear friction-sliding behavior of bearings under seismic loading.

With urban development, underground pipelines often suffer from missing records and incomplete structural information during upgrading the old pipeline network, maintenance modification, and ground facility development, which makes it difficult to accurately determine their spatial and geometric configurations and consequently limits the effectiveness of pipeline integrity inspection and assessment. To address these issues, this study proposes a pipeline geometric feature identification method based on ultrasonic guided-wave propagation characteristics and deep learning. Pipeline complexity is quantified using three indicators, including pipeline length, number of connections, and connection type. Finite element models of pipelines with different geometric characteristics are then established to obtain guided-wave responses, which are combined with experimentally acquired signals to construct the dataset. Multi-dimensional features are extracted from the guided-wave signals in the time, frequency, and entropy domains. After principal component analysis (PCA) dimensionality reduction, eight time-domain features, four frequency-domain features, and three entropy features are selected as inputs to the deep learning models. Three models, including convolutional neural network, convolutional neural network-long short-term memory network, and convolutional neural network-support vector machine, are developed and compared. The results show that the average accuracy of pipeline length estimation based on the ultrasonic guided-wave penetration principle reaches 91.66%. With experimental validation, the CNN model achieves 100% accuracy in identifying the number of connections and more than 95% accuracy in identifying connection types. In general, the proposed method demonstrates good effectiveness and robustness in pipeline geometric feature identification and provides a feasible benchmark for pipeline inspection and reconstruction of pipeline networks.

The tuned mass damper (TMD), as a passive control device has been widely applied in structural vibration control. In most studies, the TMD is analyzed as a linear device, but in fact, due to the use of large displacement and limit device, the TMD show some nonlinear characteristics. It is necessary to study the influence of these nonlinear characteristics on the control effect of TMD. In previous studies, the TMD with hardening nonlinear stiffness was usually considered, but there is less research on softening nonlinear stiffness. Therefore, this paper analyzes the nonlinear TMD with softening nonlinear stiffness. The difference between hardening nonlinear TMD and softening nonlinear TMD is compared, and the necessity of considering softening nonlinearity in design is explained. The analytical analysis of system is performed, the slowly-varying model of the system is obtained by the complex variable average method (CXA), and an approximate analytical formula for the steady-state amplitude of the primary structure is derived, and the TMD parameters are optimized by the analytical formula. Finally, it is proved by numerical method that this optimization method can improve the robustness of steady-state response in frequency.

To address the challenges of load testing in high-speed EMUs axle box bearings and the significant impact of parameter uncertainties on the accuracy of load inversion based on dynamic models, a novel axle box bearing load inversion method integrating a time-mode attention mechanism and a long short-term memory network (TPA-LSTM) is proposed. This method achieves high-precision identification of contact loads based on vibration acceleration. A 10-DOF vertical dynamic model of the entire vehicle is established, and vibration and load training samples at speeds of 100-300 km/h are obtained. The TPA module is used to discover frequency domain coupling and local dynamic changes in vibration signals from multiple measurement points. Then, LSTM is combined to capture the dependencies between long-term time series signals, establishing a deep mapping model between axle box acceleration and bearing radial load. Studies have shown that the TPA-LSTM load inversion method has strong generalization ability. The inverted loads at different train speeds are in high agreement with the actual load time-domain waveforms, with a Pearson correlation coefficient greater than 0.97, a coefficient of determination greater than 0.95, a mean absolute percentage error less than 9.0%, and a normalized root mean square error less than 2.1%. The bearing load was extrapolated from the measured axle box acceleration signal on the railway line, and the extrapolated radial force and acceleration signal spectral characteristics are consistent, demonstrating the effectiveness of the load inversion method. This research provides a theoretical basis and technical support for the acquisition of the full life cycle load spectrum and operational status monitoring of axle box bearings in high-speed EMUs.

In nuclear power plants, small branch pipes are connected to the main pipeline by socket welding, which is prone to vibration fatigue failure and thus affects structural safety. This paper investigates the vibration fatigue characteristics of small branch pipes with different defects at the weld root through experimental and numerical analyses. A vibration fatigue test platform was established, and phased array ultrasonic testing combined with modal analysis was used to obtain the critical condition for crack propagation. A finite element model of the defective small branch pipe was developed to calculate the displacement and stress distributions under different acceleration excitations. Comparative analyses were conducted on the effects of defect depth on the critical condition, stress distribution, and top acceleration and velocity, and the correlation between weld stress and top velocity was discussed. The results show that as defect depth increases, the critical condition decreases; defect depth significantly influences stress distribution, and the weld stress, top velocity, and acceleration all decrease accordingly, with the stress level following the same trend as the top velocity.

To accurately predict the stress changes in coal conveyer trestles, this paper constructs an integrated learning framework based on CEEMDAN-VMD-BO-LSTM to address the non-linearity and non-stationarity issues of stress changes. The research employs the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm to conduct multi-scale decomposition of the original stress time series, reducing the complexity of the original data. To further improve the prediction accuracy of high-frequency components, this paper introduces sample entropy to quantify the complexity of each component. After K-means clustering, the components are divided into three Co-Intrinsic Mode Functions (Co-IMF) of high-frequency, medium-frequency, and low-frequency. In response to the poor prediction effect of high-frequency components, the Variational Mode Decomposition (VMD) algorithm is used for secondary decomposition of high-frequency components. Finally, all components are input into the Long Short-Term Memory (LSTM) network for prediction, and the Bayesian Optimization (BO) algorithm is used to globally optimize the hyperparameters of the LSTM, enhancing the convergence accuracy and computational efficiency while reducing the prediction time. The research shows that the constructed CEEMDAN-VMD-BO-LSTM model has significant advantages in predicting non-linear and non-stationary stress changes. Its error is lower than that of models such as LSTM, and it has a good fitting ability for the stress changes in trestles. This provides a new technique for the health monitoring and early warning of trestles.

The steel-concrete composite structure (SCCS) is composed of steel plates and a concrete slab in a laminated arrangement. Previous studies have demonstrated that the composite structure exhibits superior anti-penetration performance, relatively thin thickness, and strong resistance to repeated impacts. However, systematic studies on the complex interaction mechanisms between projectiles and SCCS remain scarce, and the failure mechanisms of both remain unclear. To this end, based on six penetration tests and comprehensive analysis of publicly available literature data, it was found that the yield strength ratio ψ between the steel plate and projectiles is the key parameter determining their damage modes. As ψ increases, the failure mechanism of the steel plate transitions from tearing and ductile hole expansion to shear plugging. When ψ ≈ 1, increasing the thickness or number of steel plates can cause the projectile to transition from perforating the SCCS to fracturing or experiencing severe erosion and blunting. Furthermore, experimental and numerical simulation studies were conducted to investigate the effects of the length of the projectile’s cylindrical segment and the boundary conditions of the steel plate. The ratio of the cylindrical segment length to the total projectile length significantly influences the failure mode of the projectile: when the ratio is relatively high, the axial compression on the projectile head is enhanced, making the projectile more prone to structural breakup; when the ratio is relatively low, the projectile is more susceptible to erosion and blunting. The boundary conditions of the steel plate (clamped, simply supported, and free boundaries) significantly affect its deformation pattern and magnitude by altering its mechanical response.

The impact resistance characteristics of a negative Poisson's ratio sandwich panel embedded with fiber rod reinforced shear hardening material under high-velocity impact are studied using a combination of simulation and experimentation. Based on a comprehensive consideration of the constitutive relationships between the panel and core, as well as different material failure criteria, a high-speed impact finite element model of such a sandwich structure is established using ABAQUS software. The key parameters such as the impact damage area, residual velocity of the projectile, and critical velocity of the structure are also solved. In addition, a fabrication method for the tested specimens is proposed, and a high-speed impact test system is established for measurement and validation research. The study has found that the developed model has maximum errors of 3.9%, 8.8%, and 5.9% in predicting the area of structural damage, residual velocity of the projectile, and critical velocity of impact, respectively. Furthermore, the calculated impact damage mode is in good agreement with the experimental ones, fully verifying the effectiveness of such a model and its prediction results. In addition, it is observed that the critical impact velocity of the structure increases by 26.6% when it is embedded with fiber rod reinforced shear hardening material, proving that this reinforcement approach can effectively enhance its impact resistant capability.

Jointed rock masses may undergo failure under dynamic loading. In-depth investigation into the crack propagation mechanisms of jointed rock masses is of great significance for the dynamic stability evaluation and disaster prevention of underground engineering. In this study, SHPB tests on red sandstone containing pre-fabricated single cracks with different inclinations were simulated using a two-dimensional discrete element method. Crack initiation and propagation inside the specimens were tracked using high-speed photography. Contact force-chain evolution and acoustic-emission activity were examined. The results show that under impact loading, the concentration of tensile stresses in the middle and tip of the prefabricated cracks triggers bond-breakage and leads to crack initiation and propagation within the specimens. These cracks macroscopically manifest as wing and anti-wing cracks. Under the same impact velocity, when the inclination angle is 0°, spallation occurs around the prefabricated crack. With the increase of inclination angle, the area of tensile stress concentration expands, and the length of the wing crack decreases. When the inclination angle is less than 60°, the crack propagation direction intersects the loading direction. When the inclination angle exceeds 60°, the crack propagates parallel to the loading direction, ultimately resulting in a typical "hourglass" failure pattern within all specimens. It is also found that shear cracks initiate first and dominate at lower impact velocities. As the impact velocity increases, the proportion of tensile cracks within the specimen rises and gradually surpasses that of shear cracks.

Addressing the challenges to flutter stability of ultra-high bridge deck composite stiffening girders in mountainous areas, this study investigated the effectiveness and mechanisms of aerodynamic optimization measures for a slab-truss composite stiffening girder, using the Guizhou Huajiang Gorge Bridge as a case study. A combined methodology of wind tunnel testing and Computational Fluid Dynamics (CFD) was employed. First, a flutter stability test was conducted using a 1:50 scale rigid segment model of the original girder design under service stage. Subsequently, experimental studies were performed on various aerodynamic control measures, including an upper central stabilizer, wind fairings, and upper and lower horizontal guide plates. Finally, CFD analysis was utilized to examine the flutter response characteristics of the original girder section and the vibration suppression mechanisms of the aerodynamic controls. The results indicate that the critical flutter wind speed of the original girder section at 0° and positive angles of attack was below the code-specified inspection wind speed, thus failing to meet the requirements. Two combined aerodynamic control measures proved effective in enhancing the flutter performance at these angles. These were an upper central stabilizer (H=1.25 m) with a wind fairing (L=1.85 m), and an upper central stabilizer (H=1.25 m) with upper and lower horizontal guide plates (L=0.50 m and L=1.45 m, respectively). The CFD analysis revealed that at time (n+1/8)T, the airflow separated from the cantilevered slab of the original girder and reattached at the deck's leading edge, subsequently developing into a large-scale vortex at the deck's trailing edge by time (n+3/8)T. Both combined aerodynamic measures effectively suppressed this airflow reattachment at the rated wind speed. 

To address the limitations of traditional deep learning methods in bridge structural damage identification, such as insufficient feature extraction capability and low recognition accuracy under complex conditions, this paper proposes a method combining a Multi-Scale Convolutional Neural Network (MSCNN) with Sparse Multi-Head Attention for bridge damage identification. First, bridge acceleration response data is collected and preprocessed in the time-frequency domain using Fourier transform and normalization. Then, multi-scale convolution modules are employed to extract structural response features at different temporal perception scales, enhancing the model’s ability to capture both local perturbations and global trends. Furthermore, a sparse self-attention mechanism is introduced to model the global dependencies among multi-channel features in high-dimensional space, and a sparse excitation strategy is applied to focus on critical structural response regions, thereby improving the model’s representation and discrimination capabilities. The proposed method is validated using a steel truss experimental bridge and the public Z24 bridge dataset. The results show that the method achieves an average identification accuracy of 98.81% and an F1 score of 98.10% under various damage types and working conditions. Compared with traditional CNN, CNN–BiLSTM (Bidirectional Long Short-Term Memory), and CNN–BGRU (Bidirectional Gated Recurrent Unit) models, the proposed model demonstrates higher recognition accuracy and robustness, confirming its feasibility and engineering value in bridge structural damage identification.. 

The Π-shaped section is a bridge section with excellent mechanical properties, but due to its typical blunt body characteristics, it is prone to vortex-induced vibration (VIV) when subjected to wind. Based on wind tunnel tests and computational fluid dynamics (CFD) numerical simulations, this study investigates the mechanism and control measures of the two “lock-in” regions of VIV of a Π-shaped section. Firstly, the accuracy of CFD numerical simulation was verified by comparing the free vibration responses of the Π-shaped section obtained from wind tunnel tests and CFD simulation. Then, based on the analysis of vorticity and surface pressure, the mechanism of the two “lock-in” regions of VIV of the Π-shaped section was explored. Finally, the control effect of different passive control measures was studied, and the underlying mechanism was analyzed. The results show that the two “lock-in” regions of VIV of the Π-shaped section are caused by the separation vortices generated at the leading edge falling off at the trailing edge. In the low wind speed VIV region, two distinct separation vortices simultaneously exist on the lower surface, and each vortex travels from the leading edge to the trailing edge over approximately two oscillation cycles. However, the phase difference between the two separation vortices corresponds to one oscillation cycle, resulting in continuous and periodic vortex shedding at the trailing edge, which induces the vertical VIV. In the high wind speed VIV region, only one separation vortex forms on the lower surface, which moves from the leading edge to the trailing edge during a single oscillation cycle, thereby inducing vertical VIV. The addition of an inverted L-shaped guide plate changed the cause of VIV of the Π-shaped section, shifting from vortex induced by the leading edge vortex to vortex induced by the trailing edge Kármán vortex street. The addition of passive measures completely suppresses VIVs in the high-wind-speed range and suppressed VIVs in the low wind speed range within the allowable range of the specifications.

The saddle-shaped large-span steel structure is characterized by its light self-weight, high flexibility, and significant sensitivity to wind loads, making it essential to conduct field measurement studies on its wind field and wind pressure characteristics. Based on field measurement data from the Beijing National Speed Skating Oval (NSSO), this study investigates the wind field characteristics, and wind pressure properties around the saddle-shaped roof. The wind field characteristics at various locations on the roof were analyzed, along with the relationships among different statistical parameters. The spatiotemporal distribution and non-Gaussian behavior of wind pressure on the roof were examined, and a comparison was made between the normalized fluctuating wind pressure power spectral density and classical wind spectra. The results indicate that the measured wind field at the NSSO does not exhibit significant localized characteristics. Furthermore, no clear correlation was observed among the turbulence intensity, turbulence integral scale, and mean wind speed on the roof. Notably, distinct non-Gaussian characteristics were identified at the edges and corners of the saddle-shaped roof. In the low-frequency range, the measured wind pressure data agree well with classical wind spectra, with the amplitude of low-frequency components being relatively prominent.

In earthquake early warning (EEW), ground motion prediction equations (GMPEs) relying on estimated Vs30 to predict peak ground acceleration (PGA) are prone to introducing intermediate errors and losing site information. This study proposes an end-to-end Random Forest (RF) prediction method that integrates topographic and geological information, directly mapping topographic-geological features to PGA, thereby avoiding Vs30 error propagation and fully exploiting site information. The model is trained and validated using 31,404 strong motion records from the Japanese KiK-net network and compared with five typical GMPEs. Results demonstrate that the RF model improves the correlation coefficient by 18.4%~59.4% and reduces the standard deviation by 13.5%~23.8% on the test set, effectively mitigating the underestimation of large PGA values and the magnitude and distance dependency biases prevalent in traditional GMPEs. Interpretability analysis confirms that magnitude and hypocentral distance are the dominant factors for PGA prediction, while topographic and geological parameters capture complex site effects through a coupling mechanism for further correction. This research provides a novel approach for rapid and reliable PGA prediction in EEW, with significant theoretical and practical application value.

Reinforced concrete (RC) double-column piers are widely used in various small-to-medium span bridges due to their excellent overturning resistance and stability. To investigate the seismic performance of RC double-column piers under unidirectional and bidirectional loading, this study designed and conducted three sets of six scaled-down (1:3) circular-section RC double-column piers (pier heights: 900 mm, 1500 mm, 1800 mm) for unidirectional and bidirectional (rectangular loading path) cyclic quasi-static comparative tests. Test results indicate that compared to unidirectional loading: (1) Under bidirectional loading, the concrete at the pier base transitions from “lateral” crushing to “annular” crushing. At ultimate state, the height of the crushed zone at the pier base significantly increases by 40% to 110%. (2) Under bidirectional loading, the performance indicators of key points such as yield, peak, and ultimate states for RC double-column piers decreased. Specifically, the ultimate displacement decreased by 34% to 52%, and the ductility coefficient decreased by 31% to 36%. The reduction gradually diminished as the specimen's failure mode shifted from shear failure to bending failure. At a lateral displacement rate of 2.3%, the horizontal bearing capacity of RC double-column piers decreased by 14% to 30%, residual displacement increased by 21% to 41%, secant stiffness decreased by 14% to 37%, cumulative energy dissipation increased by 15% to 42%, and equivalent viscous damping coefficient increased by 23% to 72%. (3) Under bidirectional loading, the curvature of the RC double-column pier exhibits an “X”-shaped distribution pattern from pier top to pier bottom. The curvature in the plastic hinge region at the pier bottom significantly increases, while the length of this plastic hinge zone grows rapidly with increasing lateral displacement rate. (4) The seismic performance of RC double-column piers under both uniaxial and biaxial loading is significantly influenced by the shear-span ratio and reinforcement ratio. Increasing the shear-span ratio from 1.0 to 3.5 leads to increases in ultimate displacement by approximately 157% and 89%, but reductions in ultimate bearing capacity by about 68% and 67% for uniaxial and biaxial loading. Increasing the reinforcement ratio from 0.8% to 4.0% enhances ultimate displacement by roughly 21% and 35%, and ultimate bearing capacity by approximately 168% and 160%. 

To avoid the deviation of structural seismic demand caused by the traditional damping modification factor (DMF) when modifying pulse ground motions, 756 near - fault pulse ground motion records were selected. The influences of factors such as moment magnitude and pulse period (Tv) on the DMF were analyzed. An acceleration response spectrum DMF model including damping ratio (ξ), spectral period (T) and pulse period was established and compared with the actual scatter points and existing models. Research shows that the differences in DMF under different impulse period are more significant. When modeling, priority should be given to these factors. The influences of moment magnitudes are relatively small, while the influences of site classification, fault distance and source depth have the least influence. When T< 0.01 s or T >1 s, DMF converges to 1.0 and the damping effect can be ignored. When T∈[0.01 s, 1.0 s], DMF shows a negative correlation trend with ξ. T/Tv and the DMF value conform to cubic polynomials, and their fitting coefficients and ξ conform to quadratic polynomials. There are significant differences between the DMF model of pulsed seismic and the traditional DMF values, especially for high-damping isolation structures and non-structural components with low damping ratios, under the action of near-fault pulsed ground motion, the calculation results of traditional DMF models are difficult to meet the requirements of high-precision design. Adopting the DMF model corresponding to pulsed seismic will make the correction of the multi-damping seismic design spectrum more reasonable.

Bearing vibration signals exhibit non-stationarity and temporal correlation. Existing graph neural network methods predominantly rely on fixed topological structures, which struggle to adaptively capture the spatial dependencies and temporal evolution patterns of signal segments under different fault modes. To address this, this paper proposes a dynamic graph convolutional attention network architecture that integrates dynamic graph construction with spatiotemporal dual-stream coordination for data-driven adaptive modeling. The framework employs a two-phase strategy: "K-nearest neighbor candidate selection and adaptive threshold pruning" to resolve the aforementioned challenges. It first rapidly screens candidate neighbors based on temporal locality, then uses radial basis function kernels and adaptive thresholds to precisely retain high-similarity edges while ensuring connectivity through minimal edge constraints, thereby reducing graph complexity from O(N2) to O(N.K), This enables dynamic evolution of graph topology in response to input signals. Subsequently, a spatial-temporal dual-stream parallel modeling path is designed to collaboratively extract features: the spatial stream aggregates structural information under dynamic topology through multi-head attention graph convolution layers, while the temporal stream captures long-term evolution patterns via hierarchical bidirectional gated recurrent units and global attention mechanisms incorporating physical prior knowledge. Finally, feature layer fusion achieves complementary enhancement of spatiotemporal information. Cross-domain validation using one-out method on five public datasets demonstrates that the proposed method achieves an average diagnostic accuracy of 98.17%, significantly outperforming the static graph baseline. With approximately 1.2 million parameters and a single-sample inference latency of 3.6 milliseconds, the model's effectiveness is validated through diagnostic precision, cross-domain generalization capability, and computational efficiency.

The health condition of rolling bearings is critical to the safe operation of mechanical equipment. Intelligent fault diagnosis methods for rolling bearings based on domain adaptation and domain generalization have broken the limitation of traditional deep learning approaches that assume independent and identically distributed data, thereby promoting their application in scenarios with data distribution shifts, such as cross-operating conditions. However, existing methods rarely consider the easy availability of normal-condition data in the target domain, the interpretability of fault feature extraction networks, or the negative transfer caused by distribution discrepancies between simulated and real data. To address these issues, a cross-domain fault diagnosis method for rolling bearings is proposed by integrating dynamic simulation with signal processing prior knowledge. First, a four-degree-of-freedom dynamic differential equation model is established. The key parameters in the model are optimized using normal-condition data from the target domain combined with the genetic algorithm. By fusing the simulated fault signals with normal-condition data and injecting noise, a pseudo target-domain dataset is constructed. Subsequently, a diagnostic method composed of a convolutional autoencoder, decoder, fault classifier, and domain discriminator is developed. A discrete wavelet transform structure with conjugate quadrature filter properties is embedded into both the encoder and decoder. Through a composite loss function, the domain discrepancy between the source domain and the pseudo target domain is effectively reduced. Finally, experiments on a public dataset and a self-collected escalator bearing dataset demonstrate that the proposed method achieves superior diagnostic accuracy under cross-operating-condition scenarios compared with five existing methods.