Here, aiming at the problem of reduced control accuracy in electromagnetic levitation systems caused by model uncertainty and internal and external disturbances, a nonlinear backstepping control (NBC) method based on deviation- improved linear extended state observer (LESO) was proposed.Taking a single-point maglev F-track as the controlled object, a nonlinear model of a single-point electromagnetic levitation system was established, and internal and external disturbances and unmodeled dynamic state of the system were defined as total disturbances.On the basis of existing study on error LESO (eLESO), the backstepping design idea was adopted, combined with the nonlinear model of the system and the disturbance estimation of eLESO, based on Lyapunov stability theory, a nonlinear backstepping controller with disturbance compensation (eLESO-NBC) was derived to realize stabilization of various subsystems at all levels.The proposed control method eLESO-NBC was verified on a single-point maglev F-track experimental device, and compared with traditional NBC controller and LESO-NBC controller.The experimental results showed that the proposed control method eLESO-NBC keeps the fast response characteristics of NBC; when facing step disturbance, compared with LESO-NBC, the adjustment time is shortened by 65.4% and the overshoot is reduced by 36.9%.

Semi-active inerter-based suspension systems receive widespread attention due to its significant advantages in improving vehicle comfort and driving stability.However, existing control strategies lack in-depth analysis of the system dynamic response mechanism in design process.Here, to further improve vibration suppression performance of such systems, a frequency division-based control strategy was proposed.This method could combine frequency domain response characteristics of systems with and without inerter components, and use analytical analysis to reveal vibration reduction advantages of suspension systems with inerter components in specific frequency segments from a dynamic perspective.Based on these, a corresponding frequency division control scheme was constructed.Finally, the effectiveness of the proposed control strategy in improving such systems’ vibration reduction performance was verified with numerical simulation.

Accurate prediction of hydraulic bracket pressure signals has important theoretical significance and application value for improving the safety production level of coal mines.However, existing methods still have certain limitations in dealing with complex time-frequency characteristics of pressure signals.Here, a stress signal prediction framework called time-frequency transformer network (TF-TransNet) was proposed to integrate time-frequency domain analysis and deep learning.Firstly, Gaussian moving average filtering was used to denoise the original pressure signal, and Granger causality analysis was used to screen the most predictive feature variables.In feature representation stage, channel attention mechanism was introduced to dynamically adjust feature weights, and a feature fusion module was designed to enhance interaction modeling ability among variables.The core of the model was an innovative time-frequency fusion encoding and decoding structure, it could combine fast Fourier transform (FFT), long short-term memory (LSTM) network and probability sparse attention mechanism to realize deep mining of multi-dimensional time-frequency features of pressure signals.FFT could provide global information in frequency domain to reveal periodic patterns hidden in signal.LSTM could effectively capture long-term time sequential dependencies.Probabilistic sparse attention mechanism could guide the model to focus on key time-frequency feature information.Taking actual hydraulic bracket pressure data of Fucun Coal Mine in Zaozhuang, Shandong as the basis, TF-TransNet model was compared with traditional LSTM and Transformer models, respectively.Contributions of various modules to model performance were verified with ablation experiments.The experimental results showed that in a 12-step prediction task, compared to LSTM and Transformer, TF-TransNet can reduce root mean square error by about 28.10% and 20.48%, respectively, reduce mean absolute error by about 33.62% and 24.00%, respectively and improve R2 index by 0.482 6 and 0.301 0, respectively.

A sleeve-type all-bolt assembly RC wallboard is proposed in this paper. The connection between the upper and lower wallboards adopts a high-strength bolt-steel plate composite structure, and the sleeve connector is pre-embedded to achieve rapid assembly of the structure. Four kinds of specimens were designed, and the horizontal pushover and hysteretic tests were carried out. The seismic performance indexes such as failure phenomenon, hysteretic curve, skeleton curve, stiffness degradation and energy dissipation capacity of RC slabs were obtained. The results show that the specimen has high bearing capacity and stiffness in pushover and hysteretic tests, and the overall stability is good. The damage is mainly concentrated in the corner of the connector, forming an 'X' -shaped cross-crack zone. After increasing the number of cover plates, the ultimate bearing capacity of the specimen increased by 24 %, and the stiffness increased by 17 %. The hysteresis curve is changed from anti-S shape to spindle shape, and the energy dissipation capacity is increased by 16 %. The new connection can complete the stress transfer well, meet the rapid assembly and safety requirements of the wallboard structure, and provide technical support for the application of this type of structure. 

Two piezoelectric dynamometer are designed for vibration isolation performance testing of shipboard vibration isolators using force transmission rate as the vibration isolation performance index of the vibration isolators. The theoretical physical model of the piezoelectric dynamometer is established, the theoretical dynamic characteristics of the piezoelectric dynamometer are analyzed, and the influence of the intrinsic frequency on the dynamic force measurement of the dynamometer is investigated. Based on the piezoelectric effect, the structure of the piezoelectric dynamometer is designed, and some structural dimensions are optimized by parametric simulation, and the static and dynamic performance of the dynamometer is verified by finite element simulation. Through the static and dynamic calibration experiments, the sensitivity of the two piezoelectric dynamometer is verified to be 3.59pC/N, the nonlinear error is less than 0.15%, the repeatability error is less than 0.61%, the dynamic measurement error is less than 5% in the frequency range of 0~200Hz, and the measurement threshold is less than 0.01N, and finally, the force transmittance curves of the vibration isolators are obtained through the swept-frequency excitation experiments.

Existing negative Poisson's ratio (NPR) structures often feature complex geometries and rely on costly additive manufacturing, limiting their engineering applications due to high costs and low efficiency. Inspired by tradi-tional 2D NPR honeycombs, a self-locking structural unit cell was designed and assembled into a novel NPR structure. Its quasi-static compressive properties were investigated via theory, experiments, and simulations, validating the reliability of plateau stress predictions. The self-locking structure exhibited significantly higher specific energy absorption (SEA: 1217.9 J/kg) than its unit cell (423.5 J/kg). Key parameters (wall thickness, inclination angle) were optimized, with 1 mm thickness and 60° angle yielding the best performance. Compared to traditional 2D NPR honeycombs, the self-locking structure achieved 51.7% of their SEA. Fabricated through conventional methods and flexibly assembled, it offers a low-cost, lightweight, and high-energy-absorbing metamaterial strategy.

关键词：输电线路；覆冰导线；气动特性；气动稳定性；脱冰跳跃高度

Placement optimization of sensors is a key step for health monitoring systems design. Existing research mainly considers single-axis accelerometers, ignoring information redundancy and cost. To address these issues, a multi-objective optimization placement model for triaxial accelerometers is proposed, with optimization objectives including the sensor placement effectiveness, information redundancy, and sensor placement cost. Furthermore, an improved genetic particle swarm algorithm is proposed to solve the model. The correctness of the model is verified through an engineering case study involving the fast gate system of a hydropower station. Results show that, compared with three classical algorithms, the overall performance of the initial population improves by an average of 45.73%, enhances solution quality by an average of 46.43%, and increases algorithm stability by an average of 78.96%.

Earthquake simulation shake tables typically use three-variable control. Manual tuning is complex due to specimen effects, and tuned performance relies heavily on inverse model compensation. An auto-tuning method is proposed to address this. Based on closed-loop frequency response analysis, an auto-tuning function extends bandwidth while ensuring stability. A notch filter suppresses resonance from table-specimen interaction. After identifying the open-loop transfer function, parameters are optimized using a genetic algorithm. Results show the method enables auto-tuning for specimen-loaded tables and improves seismic waveform accuracy.

The mode number (K) and penalty factor (α) are improper sets can seriously affect signal VMD performance. The existing improved VMD methods cannot simultaneously consider speed and accuracy when optimizing parameters. Moreover, the avoidance of signal under-decomposition and over-decomposition, and the minimization of the information difference between components and the original signal, have not all become the objectives of parameter optimization. As a result, the determined optimal combination of K and α has not sufficiently improved the signal decomposition performance. To address this issues, a signal VSS-VMD algorithm is proposed to optimize K and α. The energy loss coefficient is used to evaluate the signal under-decomposition, the cross-correlation coefficient and kurtosis are combined to evaluate signal over-decomposition, and the information entropy difference between the components and the original signal is employed to evaluate the ability of the component to represent the original signal. The step size of α gradually decreases, a larger step size is first used to quickly find the sub-optimal parameter combination for narrowing down the optimization range, and then the smaller step size is used to accurately find the optimal parameter combination. Compared with the three recently reported improved VMD methods, the decomposition results of simulated signals and multiple measured signals show that the optimal K and αdetermined by VSS-VMD method are extracted signal components complete. This approach without signal under-decomposition and effectively avoid over-decomposition, while demonstrating superior noise suppression effect. The energy difference between the components and the original signal, as well as the orthogonal index between the components, are minimal, and the ability of the components to characterize the original signal is strong. This method ensures accurate optimization while significantly reducing time in the case of a large parameter optimization range, providing important reference for improving signal VMD performance and related application research.

Bolt connections are widely used in engineering structures. Preload condition directly affects structural stiffness, stability, and operational safety. To address the insufficient sensitivity of conventional guided wave energy methods under high preload, a spectral statistical modeling approach was proposed using chirp excitation signals for non-destructive identification of bolt conditions. Guided wave responses were collected under torque levels from 50 to 90 N•m. Three spectral features—centroid, bandwidth and kurtosis—were extracted to characterize the relationship with preload. The results show that the spectral centroid shows a stable and monotonically decreasing trend with the preload force. The exponential model can accurately fit this relationship. The consistency of the three groups of repeated experiments is good, the coefficient of determination R^2 is as high as 0.9948, the standard deviation of the spectral centroid is approximately 0.86kHz, and the relative dispersion does not exceed 0.6%, fully demonstrating the stability and reliability of the model. Frequency band energy analysis further confirmed a shift in spectral energy toward lower frequencies.Bandwidth and kurtosis also exhibited consistent monotonic variation with preload. The proposed method offers asimple structure, strong noise resistance, stable trends, and clear physical interpretability. It is suitable for onlinemonitoring and condition evaluation of bolted structures under complex operating conditions.

A pneumatic catapult scheme based on two-stage energy-release is proposed to address the issues of high overload and low exit velocity in single-stage cold launch. A catapult calculation model is established, and the effectiveness of the model is verified through experiments. Conduct simulation research on the interior ballistic characteristics of a two-stage energy-release cold launch system, obtain load interior ballistic characteristic parameters, analyze the influence of the initial volume of the low-pressure chamber, valve inner diameter, and power unit energy-release interval and initial energy configuration on the ejection interior ballistic characteristics. The results indicate that the low-pressure chamber has a significant impact on load overload and a relatively small impact on the outlet velocity; The inner diameter of the valve has a significant impact on the peak load overload, with a 30mm increase in inner diameter resulting in a 17.89% increase in load discharge speed; The energy-release interval of the power unit is shortened by 70ms, and the total time for the load to exit the cylinder is shortened by 10.19%; Under the same initial energy conditions, the two-stage energy release cold launch scheme can reduce the peak overload by 14.85% and increase the exit speed by 5.02% compared with the single-stage ejection scheme. The results of this study provide reference value and significance for optimizing the design of pneumatic ejection systems, and further provide new research directions and insights for designing new cold launch systems and enhancing the interior ballistic characteristics of ejection systems.

To address anomalies or missing data in bridge health monitoring caused by environmental interference or sensor failures, this study proposes a hybrid approach based on the Sparrow Search Algorithm (SSA) for co-optimizing Variational Mode Decomposition (VMD) and Gated Recurrent Units (GRU) to reconstruct impaired bridge monitoring data. This study explores the use of the Sparrow Search Algorithm (SSA) to optimize the decomposition level K and the penalty factor α in Variational Mode Decomposition (VMD) to accurately extract bridge structural responses. SSA is also employed to optimize the key hyperparameters of the Gated Recurrent Unit (GRU) network. Through iterative training, the model achieves its optimal state, after which the decomposed signals are used as inputs for predictive recovery, reconstructing missing bridge monitoring data. By comparing the results with those from a standalone GRU model and a VMD-GRU hybrid model, the proposed method's scientific validity and practical utility are evaluated using error metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the Coefficient of Determination (R²). Research indicates that the proposed method can obtain the optimal parameter combination without empirical guidance, achieving a root mean square error of 6.0702% for the deflection test set and merely 0.15% for the strain test set. This approach is suitable for reconstructing abnormal or missing monitoring data in bridge structures, enhancing data quality and usage accuracy, thereby providing a methodological foundation for bridge health monitoring and decision-making.

Accurate finite element models hold significant engineering value for the design and performance optimization of antenna beam structures, with the core objective of precisely controlling the resonance frequency range to avoid resonance issues induced by circuit excitation. Although the antenna beam structure is relatively simple with few parameters, the influence of end stiffness and constraint conditions leads to significant deviations between finite element modal analysis results and experimental data. To establish a more precise model, this study employs hierarchical modal testing to trace the primary sources of model errors. The results reveal that improper boundary condition settings are the key contributor to modeling inaccuracies. By applying the particle swarm optimization (PSO) algorithm to calibrate boundary constraint parameters, the average error between simulated and experimental modal frequencies was reduced to 2.11%, with a mere 0.76% deviation observed for the critical vertical bending mode frequency. The refined finite element model provides a reliable foundation and practical engineering value for subsequent structural optimization of the antenna beam.

Suppressing vortex-induced vibration of bluff bodies is a research hotspot; inspired by the pappus structure of dandelion seeds, a vertical orifice plate structure is proposed. Nine groups of schemes with different orifice plate parameters are designed using the orthogonal test method, and their vibration and load reduction performance are analyzed through computational fluid dynamics numerical simulation. The characteristics of the optimal scheme under different reduced velocities are also explored. The results show that the primary and secondary factors affecting the performance of the orifice plate are, in order, plate-cylinder spacing, orifice plate length, and porosity. The optimal scheme is a plate-cylinder spacing of 0.6D, a porosity of 30%, and an orifice plate length of 1.5D, which can reduce the amplitudes of the cylinder in the streamwise and cross-flow directions by 90.59% and 98.71% respectively, and reduce the root mean square value of the lift coefficient and the average value of the drag coefficient by 91.45% and 30.42% respectively. At low reduced velocities, the optimized orifice plate weakens vortex shedding intensity through a porous dissipation mechanism, thereby improving the flow field stability and the pressure distribution on the cylinder surface, reducing the vortex-induced loads, and suppressing the cylinder vibration. At high reduced velocities, the orifice plate fails in regulation. The jet flow through the openings intensifies flow field disturbances, prompting the generation and shedding of larger-scale vortices at the cylinder's trailing edge and on both sides of the orifice plate. This leads to an uneven pressure distribution on the cylinder surface, enhanced vortex-induced loads, and ultimately intensified cylinder vibration.

In structural health monitoring (SHM) systems, sensor faults, communication interruptions, and environmental disturbances often lead to large-scale continuous data loss. Existing data reconstruction studies primarily focus on discrete missing values and generally lack effective mechanisms for assessing reconstruction accuracy when ground truth is unavailable, limiting the practical applicability of their results in engineering scenarios.This paper proposes a differential Transformer-based method for acceleration data reconstruction and accuracy self-assessment in SHM. The method incorporates a differential attention mechanism, combined with a random masking strategy and positional encoding, and integrates both a data reconstruction module and an accuracy prediction module to achieve high-precision recovery and reliability evaluation under high missing rates. The influence of training parameters, network architecture configuration, and inter-sensor correlations on reconstruction performance is further investigated to systematically assess the model’s adaptability and robustness under various missing patterns. Experimental validation on both the Dowling Hall bridge dataset and a simulated three-span continuous beam model demonstrates that the proposed method maintains stable reconstruction performance and accurate error prediction under high missing rates, highlighting its strong generalization capability across structural types and sensor layouts, as well as its potential for practical engineering deployment.

Wheel/rail rolling contact is one of the most critical contact pairs in the railway system. Performing a robust, accurate and fast wheel/rail rolling contact model is of great importance for precisely calculating train-track non-linear interactions and wheel/rail tread wear. The vehicle-track spatially coupled dynamics models are extended based on fundamental of vehicle-track coupled dynamics, in which the Hertzian, ANALYN, MKP and CONTACT models are considered. The effect of these wheel/rail dynamic interactions subjected to these numerical models is compared with the track random irregularity, wheel polygonal wear and rail welding irregularity. The results point out that non-Hertzian models are capable of higher accuracy in comparison to the Hertzian model in vehicle-track interaction simulations, especially under the excitations of wheel/rail short-wave or pulse irregularity. However, the Hertzian model could be a compromise for its high computational efficiency acceptable precision. This study can provide a theoretical reference for the selection of wheel/rail contact model in wheel/rail dynamic interaction and wear predictions.

In the marine field, the dynamic stiffness of bellows is crucial for system reliability and performance optimization, but the theory, test and simulation of the dynamic stiffness of marine multilayer metal bellows under internal pressure load have been carried out relatively little at home and abroad. In this paper, DN250U-type multilayer metal bellows is selected as the object to study the test and simulation method of metal bellows under internal pressure load, and the axial dynamic stiffness characteristics of the bellows under internal pressure load are studied through the combination of test and simulation and the dynamic stiffness at the origin is forecasted. According to the comparison between the dynamic stiffness test results and the dynamic stiffness simulation results under the test condition, it can be seen that the trends and values of the dynamic stiffness curves at the origin and span points of the test and simulation from 0 MPa to 0.5 MPa roughly coincide with each other, so the feasibility of the equivalent spring method for calculating the dynamic stiffness of the bellows under the internal pressure load is proved. Relying on the test and simulation results, the influencing factors of the dynamic stiffness at the origin are discussed and corrected on the basis of the existing kinetic theory, and the correction formula of the dynamic stiffness of the multilayer U-shaped metal bellows is proposed for the dynamic stiffness of the multilayer U-shaped metal bellows under the internal pressure load, which can be used as a prediction of the dynamic stiffness performance of the multilayer U-shaped metal bellows in the engineering application.

To investigate the steady-state response intervals of a misaligned rotor system under model uncertainty, a deterministic dynamic model was established using the finite element method. A nonparametric uncertain model was developed based on random matrix theory and the principle of maximum entropy. Positive-definite random matrices were generated with matrix polar decomposition. The uncertainty of the system was characterized using the dispersion parameter identification method. Sweep frequency experiments were carried out on a rotor test rig to verify the accuracy and feasibility of the proposed method. The angle between the two half-couplings was taken as the key misalignment parameter to compare the nonlinear response intervals of the uncertain rotor system.

In order to solve the problem that the traditional indicators describing bearing degradation cannot accurately reflect the bearing degradation state in the whole life cycle of the bearing, a Gaussian mixture model (GMM) combined with fusion divergence method was proposed to evaluate the degradation state of rolling bearings. Firstly, VMD decomposition is performed on the original data to remove the interference component in the signal, and the GMM of the sample data after VMD decomposition is established., and the GMM of the healthy samples at the initial time was used as the benchmark, and the GMM of the remaining sample data was used to compare the differences with the benchmark. Then, different types of divergence were used to quantify the difference between the GMM of the health data and the GMM of the rest of the data, and in order to combine the advantages of each divergence index, the obtained quantitative indicators of different divergence were obtained by adaptive weighting fusion method to obtain the comprehensive health index HI. Finally, the Chebyshev inequality principle is used to establish a threshold to evaluate the degradation state of the bearing. The experimental results show that the comprehensive health index can accurately and timely judge the degradation state of the bearing, and compared with the traditional time-domain index and entropy index, its sensitivity is high, the fluctuation is small, and the monotony is good, which can better reflect the degradation state of the bearing, and provides a new idea for the evaluation of the degradation state of the bearing. 

We propose a graph neural network algorithm based on multi-source multi-feature nodes to address the issues of non-stationarity, difficult feature extraction, and fault classification in bearings under variable speed conditions. This method uses an adaptive weighting algorithm to combine the Gram angle difference field to optimize the Gram graph, and realizes the combination of vibration signals and speed information. Then, the Swin Transformer mechanism is improved to extract image features, and a structural graph is input into the graph convolutional neural network model for fault diagnosis, thereby improving the accuracy of bearing fault diagnosis in variable speed conditions. The experimental results show that the fault diagnosis rate of convolutional neural network, long short-term memory, Transformer, and ordinary GCN deep learning models in variable speed datasets is lower than that of the model proposed in this paper. The proposed method achieved an accuracy rate of 99.9% in the Ottawa public dataset, and also achieved an accuracy rate of 99% in the self-test dataset.

The Lamb wave dispersion characteristic curve is an important reference for acoustic emission technology to locate the damage source of bearings, however, the traditional analytical method or numerical method cannot obtain the complete Lamb wave dispersion characteristic curve when analyzing the propagation characteristics of guided waves in bearings. To address this problem, this paper adopts the semi-analytical finite element method to solve the Lamb wave dispersion characteristic curve in the bearing. The dispersion curve is compared with the time-frequency diagram of the acoustic emission signal to identify the propagation modes of the signal in the bearing, and according to the frequency information contained in the dispersion curve to separate the signals of different frequencies, the wavelet coefficient mode-maximum method is used to calculate the time of arrival of the signals of different frequencies at the sensors, and the propagation speed is combined with the difference of the time of arrival to calculate the relative angle between the source of the damage and the sensors, so as to locate the source of damage in the bearing. In addition, in order address the positional uncertainty of single-sensor localization, secondary localization is carried out by means of moving the sensor, and the overlapped area of the two localization results is the real position of the bearing damage source. The effectiveness of the proposed method is verified with the broken lead experiment and the test of simulated damage source localization.

Centrifugal pumps, fans, and other mechanical equipment transporting various media are among the primary sources of vibration and noise on ships. The excitation sources generated by high-speed shaft rotation-such as hydrodynamic forces and unbalanced forces-transmit through the shaft and supporting structures, leading to foot vibrations of the equipment. These vibrations are a major focus in the development of low-noise machinery and ship vibration/noise control. To address low-frequency line-spectrum vibration in marine machinery caused by rotor dynamic imbalance and uneven circumferential loads, this study takes a pump unit as the research subject and conducts active control simulations based on the Multi-Channel Frequency Domain Block Least Mean Square(MCFBLMS) algorithm. The simulation results demonstrate that active control of the pump rotor can effectively suppress foot vibration induced by multiple excitation sources. Furthermore, experimental studies on active control were carried out using an electric motor as the test subject. The results show significant attenuation in line-spectrum amplitudes under various conditions, including single-tone, multi-tone, and bidirectional control scenarios. The proposed active control method for low-frequency shaft vibrations provides a novel technical approach for mitigating low-frequency line-spectrum vibrations in marine machinery.

Aiming at the problems that the compound fault samples of key mechanical systems such as axle boxes and gearboxes of urban rail trains are lacking, and the compound fault features are difficult to effectively extract in the multi-component coupling scenario, resulting in the low diagnostic accuracy of the existing deep transfer methods, a compound fault diagnosis method based on intrinsic mode function (IMF) selection and improved two-stage transfer learning is proposed. Firstly, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm is adopted to decompose the compound fault signal. Wavelet packet transform is utilized to achieve multi-band feature enhancement, and IMF components that best match the source domain distribution are screened through the particle swarm optimization (PSO) algorithm. Then a two-stage hybrid attention classification model is established, and the two-stage model is trained respectively using source domain single-fault data. The classification results from the first stage are applied to the second stage, enabling precise identification of complex faults through optimization with a two-stage mutex-label loss function. The experimental results show that the proposed method achieves high average recognition rates in both bearing compound fault diagnosis and gear-bearing component-level compound fault diagnosis tasks, achieving the transfer diagnosis from single faults to compound faults.

The inner raceway (eccentric circular surface of the crankshaft) of the needle roller bearing of the RV reducer is prone to material fatigue spalling failure, and there are characteristics of random and multiple failures between multiple groups of needle roller bearings. In order to effectively reveal the dynamic response characteristics of RV reducer in the combination mode of multiple sets of needle roller bearing defects, based on the theoretical model of RV reducer contact multi-body dynamics, a calculation method for the time-varying contact displacement of geometric defects of rolling elements and raceways was proposed, and the dynamic model of RV reducer with multiple defects of needle roller bearings was established by considering the influence of different combination modes of needle roller bearing defects. On this basis, the dynamic response of the crankshaft, cycloidal gear and output planetary carrier was simulated and analyzed by taking the RV-20E reducer as the object, and the accuracy of the model was verified by vibration test. The results show that the vibration characteristics of the crankshaft and the output planetary carrier are greatly affected by the defects. In the frequency domain, there are mainly modulation phenomena caused by the frequency of the crankshaft and the cycloidal gear near the eigenfrequencies. In different defect combination modes, the vibration signal of the transmission will present a modulation phenomenon characterized by different octanations of the crankshaft frequency.

Seismic intensity measures (IMs) are a crucial component in structural damage assessment. To identify the optimal IMs for corrugated steel utility tunnels, this study focuses on large-span horizontally elliptical corrugated steel utility tunnels. Four finite element models (FEMs) incorporating soil–structure interaction were developed. Using Latin hypercube sampling and incremental dynamic analysis, a comprehensive fragility model was constructed for the horizontally elliptical corrugated steel utility tunnel. Relative deformation rates in various structural directions were selected as damage measures (DMs). The effectiveness, practicality, and efficiency of 21 commonly used IMs were evaluated. Additionally, the Random Forest (RF) algorithm and SHAP method were employed to explore the correlation between IMs and DMs. The results indicate that velocity-based IMs, which account for structural characteristics, exhibit the highest efficiency, while ratio-based IMs perform the worst. Velocity-based IMs significantly outperform acceleration-based and displacement-based IMs. They show a generally strong correlation with DMs, making them suitable for evaluating damage in large-span horizontally elliptical corrugated steel utility tunnels. The choice of DM significantly affects the fragility analysis results; the vertical relative deformation rate is recommended as the primary DM.

To meet the seismic performance requirements of long-span continuous girder bridges in high-intensity seismic zones, a innovative sliding-rotational composite friction bearing (RCFB) is proposed on the basis of plane sliding friction bearing (SFB). Its structural structure and motion behavior are introduced, and the mechanical model is derived. Then, a model is established based on the ABAQUS platform for numerical analysis, and quasi-static tests are carried out to verify the feasibility of the bearing scheme and the correctness of the mechanical model. Finally, the seismic performance of a long-span continuous girder bridge in 8-degree seismic zone is studied by using nonlinear time-history analysis. The results showed that the RCFB bearing has simple structure and clear functions, The numerical analysis and test results are in good agreement, and are highly consistent with the theoretical mechanical model. The RCFB exhibits excellent energy dissipation performance, compared to the SFB, the structural internal force response increases to some extent, but the relative displacement between the pier and girder can be effectively controlled. The friction coefficient of the bearing and the critical displacement of its two-stage motion significantly influence the structural response. In practical engineering, seismic design objectives that balance internal forces and displacement responses can be achieved by appropriately determining bearing parameters.

The shock energy of CO2 jetting from liquid CO2 cartridge into borehole is the unique power to fracture coal and rock mass. Researching on the distribution characteristics of shock energy in borehole is of great significance in the study of rock cracking and its mechanism. Experimental study of shock energy released by gas-phase shock of in-situ prototype liquid CO2 cartridge has been performed by utilizing self-developed blasting system. Considering the isentropic flow of CO2, the mechanical model for supercritical CO2 circular jet generated by liquid CO2 phase transition was established in this paper and combined with the experimental results, the regulars of distribution have been comprehensively analyzed on shock energy. The results showed that the high-velocity supercritical CO2 jet flow deriving from liquid CO2 phase transition was compressed because of the limitation of the blasting hole-wall while generating continuous impulse shock waves. All time histories of shock energy away from the nozzle of liquid CO2 cartridge performed markedly asymmetric inverted V-type with intermittently instant rises and falls owing to the interaction between supercritical CO2 and shock waves. The shock energy presented rapid rise and fall with triangular pulses at the macro level. The shock pressure, that was up to 230 MPa, distributed near the nozzle, while both pressurization and depressurization rates of pressure curve near the nozzle were much greater than those of the pressure curves away from the nozzle. The shock energy had trapezoidal pulse characteristic around the seepage hole which was the location of blasting fracture, and the gas-phase shock energy mainly gather round the blasting fracture. The closer to the blasting crack, the higher the blasting energy at this position. In addition, the shock pressure surrounding the blasting fracture reached up to 79.16~84.97 MPa, meanwhile pressure holding above 20 MPa could maintain about 220 ms. Thus, CO2 gas-phase shock was a typical intermediate-frequency dynamic load. Based on the distribution regulars of shock energy obtained by the experiment, a new cracking method, termed as gas-phase shock of supercritical CO2 by concentrated jet impact, was proposed to improve current technology. The results obtained in this paper can provide guidance for theoretical research and in-situ application on gas-phase shock of liquid CO2 cartridge.

Wedge excavation blasting is widely used in excavation work due to its concentrated blasting energy and high utilization rate of blast holes. To investigate the effect of in-situ stress on wedge-shaped excavation blasting, this study adopted the uniaxial compression method under extreme conditions, based on the excavation theory under confining pressure, and used model experiments and finite element numerical simulation methods to explore the mechanical mechanism during the blasting process. The research results indicate that uniaxial compressive stress has a guiding effect on the generation and propagation of cracks caused by wedge-shaped groove blasting, and the direction of crack generation and propagation is consistent with the direction of uniaxial compressive stress after blasting. Uniaxial compressive stress will promote the large fragment rate of groove fragmentation, with a significant effect on fragment sizes larger than 9.5mm and no significant effect on fragment sizes smaller than 9.5mm. Uniaxial compressive stress has a relatively small impact on the peak Mises stress in the early stages of blasting, but has a significant effect in the later stages of blasting. When uniaxial compressive stress is applied perpendicular to the long axis of the blasthole (T2), the cavity volume reaches its maximum. However, when uniaxial compressive stress is applied perpendicular to the short axis of the blasthole (T3), the cavity volume increases. There is no significant change in the depth range after the application of uniaxial compressive stress, indicating that the damage along the depth direction after blasting is almost unaffected by uniaxial compressive stress. The research results provide a reference for optimizing the cut design under in-situ stress conditions.

To identify the loosening failure boundary of bolted joint structures under complex shock loads, a boundary determination method based on evolutionary power spectral density (EPSD) is proposed. First, shock experiments were carried out on a typical bolted joint structure under complex shock loads, and the strain responses under different tightening torque conditions were recorded. Based on wavelet transform, the EPSD matrix of the shock response was derived, and the root mean square values of strain at monitoring points were calculated as a function of tightening torque, revealing the evolution pattern of bolted joint loosening. A refined finite element model of the bolted joint structure was then established and validated through comparison with the strain responses obtained from both time and frequency domains in experiments. On this basis, the influence of shock load parameters on the loosening failure boundary was analyzed using the finite element model. The results indicate that as the knee frequency of the shock response spectrum increases, the loosening failure boundary of the bolted joint structure tends to rise. Under shock loads with lower knee frequencies, the structure is more prone to loosening failure.