To investigate the impact of the shell-side inlet spiral deflector (SD) on the heat transfer performance of the spiral copper tube (SCT) in a heat exchanger, both the heat transfer capacity of the SCT and the overall performance of the SCT heat exchanger were analysed.Two configurations were examined: the one with the SD and SCT installed in the same direction (RSD-SCT heat exchanger) and the other with them installed conversely (CSD-SCT heat exchanger).The results show that installing the SD at the shell-side inlet increases fluid turbulence, significantly enhancing both the SCT’s heat transfer capacity and the overall performance of the heat exchanger.Under various operating conditions, the SCT in both configurations achieves vibration-enhanced heat transfer, with effectively rising alongside the inlet flow velocity.Under the equal inlet flow velocity condition, the RSD-SCT exhibits more pronounced vibration, leading to superior vibration-enhanced capability and better overall performance compared to the CSD-SCT.

The class imbalance problem caused by scarce data of damage condition significantly affects the performance of deep learning models during the early stage of structural damage.To this end, a damage identification method integrating denoising diffusion probabilistic models (DDPM) and convolutional neural networks (CNN) was proposed.Firstly, an initial dataset was established using structural vibration signals, followed by the DDPM-based generation of additional damage samples and quantitative evaluation of their quality through a spectral cosine similarity metric.Secondly, a CNN model was trained on the generated samples and optimized via a weighted cross-entropy loss function to improve the recognition ability for minority class samples.Then, the performance of the CNN model was evaluated using an independent test set, and the t-distributed stochastic neighbor embedding was adopted for feature visualization.Finally, the feasibility of the method was validated through a three-span continuous beam numerical experiment.The results demonstrate that the DDPM-based data generation method can generate high-quality samples of damage condition and significantly enhance the classification performance and noise robustness of the CNN model.When the class imbalance ratio increases from 5.0% to 100.0%, the average identification accuracy of vibration data at some measurement points improves from 44.0% to 94.5%, with the coefficient of variation decreasing from 20.9% to 3.0%.Additionally, high classification accuracy can still be achieved even using vibration data with a low SNR of 10 dB.

In response to the issues of boom vibration and discontinuous motion trajectories caused by the traditional gradient projection method in the autonomous operation of hybrid high-altitude lighting vehicles, as well as the challenges of spatial obstacle avoidance in multi-obstacle environments, a boom motion control and obstacle avoidance method was proposed based on an improved gradient projection approach.To suppress boom vibration, the conventional gradient projection method was reformulated into a two-part control strategy, where control weights were dynamically adjusted according to the proportion of the telescopic boom’s extension, thereby ensuring smooth motion.Furthermore, a terminal position error minimization algorithm was incorporated to enhance trajectory tracking accuracy.For obstacle avoidance, superquadric surface functions were employed to model environmental obstacles, and a pseudo-distance metric was used in place of the Euclidean distance to assess the proximity between the boom and obstacles.An escape velocity component was introduced based on the minimum pseudo-distance criterion to effectively prevent collisions.Simulations and physical prototype experiments confirm that the proposed method achieves stable boom motion while ensuring effective obstacle avoidance in complex environments.

This paper investigates the steady-state responses of subharmonic parametric resonance in an axially moving functionally graded Timoshenko beam undergoing a magnetic field. By incorporating nonlinear terms and variable-speed conditions, a nonlinear dynamic model accounting for viscoelasticity and magnetic field effects is established. The subharmonic parametric resonance behavior is analyzed using the direct multi-scale method. This analysis elucidates the influence of parameters such as magnetic field intensity, viscoelastic coefficient, functionally graded index, and velocity fluctuation amplitude on the steady-state response. The results demonstrate that increasing the magnetic field intensity and viscoelastic coefficient reduces the instability region of the trivial solution, while increasing the functionally graded index and velocity fluctuation amplitude expands this region. The validity of the direct multi-scale method is confirmed by comparison with results obtained via the Galerkin truncation method, with both methods showing consistent qualitative trends. These findings provide a theoretical basis for engineering applications of functionally graded materials in magnetic field environments.

Due to geological processes, the material properties of grotto surrounding rock exhibit significant spatial non-stationarity, making it difficult for traditional seismic response analysis methods based on deterministic homogeneous assumptions to accurately assess their true behavior and potential risks under seismic actions. This paper proposes a stochastic finite element analysis framework that considers the spatial non-stationarity of material parameters. A non-stationary random field model reflecting the deterioration gradient was established based on field data and geostatistical methods. A comparative analysis of the dynamic response of the grotto using both stationary and non-stationary random field models was conducted. The results indicate that material non-stationarity significantly affects peak and residual stresses in a location-dependent manner, particularly altering the mean spectral characteristics and frequency-domain energy distribution in severely deteriorated zones. Specific geometric locations in the grotto exhibit broadband acceleration amplification, which is dominated by geometric focusing and topographic effects, surpasses the impact of material deterioration, and is not caused by resonance. The response indicators show high randomness and non-Gaussian characteristics. This study reveals that the non-stationary random field model can capture key mechanisms overlooked by traditional models, providing a scientific basis for the refined seismic safety assessment and targeted protection of grottoes.

The independently developed fully hydraulic variable valve system (FHVVS) enables the diesel engine to achieve a cylinder deactivation thermal management mode with complete closure of the intake and exhaust valves. Vibration acceleration and cylinder pressure were measured on a test bench for CDA-2, CDA-3, and throttle valve modes. Frequency domain, vibration acceleration, and cylinder pressure analyses were performed of these three operating modes under steady-state conditions. Order analysis and order slicing analysis were performed on the vibration acceleration of CDA-2, CDA-3, and diesel engines under acceleration conditions without throttling. The results show that: Under steady-state conditions, the second-order vibration in the two-cylinder deactivation mode(CDA-2) becomes the primary source of vibration, while the 1.5-order vibration and its harmonics in the three-cylinder deactivation mode(CDA-3) become the primary source of vibration. Compared to the throttle valve mode, the vibration acceleration and cylinder pressure in the CDA mode have significantly increased. The vibration acceleration and pressure in the cylinder of the CDA mode have increased significantly; When a diesel engine operates in CDA mode at common operating speeds above 800r/min, CDA mode is more likely to cause system resonance. Therefore, it is necessary to strengthen vibration reduction measures for the diesel engine itself and improve vibration matching with the working machinery.

Structural damage detection based on monitoring data is crucial for bridge operational safety. However, the lack of labeled data in real bridge monitoring often results in insufficient accuracy of damage detection methods. To improve the accuracy of damage detection in bridge structures under small sample monitoring data, this paper proposes a structural damage identification method based on feature-transferable digital twins. The method uses digital twin technology to reduce the error between numerical models and real structures and expands the sample size of damage states through numerical models, forming a physics- and data-driven approach for bridge structural damage identification. In the absence of labeled data, the method applies transfer learning based on damage-sensitive and domain-invariant features to train numerical model and real structure data, generating labels for actual monitoring data, thus overcoming the limitation of traditional methods that only reduce errors. The effectiveness of the proposed method is validated through test data from a cable-stayed bridge scale model. The results show that the feature visualization process observed the gradual alignment of source and target domain features in low-dimensional latent space, significantly reducing the discrepancy between the two domains. Additionally, the learning mechanism of unsupervised domain adaptation is revealed, solving the cross-domain damage detection problem, and accurately identifying structural damage locations without labeled training data.

In the reliability modeling of wind turbine generators, the limitations of independent and binary states among core components were taken into account. Based on the physical characteristics of the degradation of the four core components in wind turbine generators and the property of failure dependence, by introducing the failure influence factors existing between components, a multi-state Markov model of the wind power generation system considering failure dependence was proposed. Drawing on the aggregation stochastic process theory in ion-channel mathematical modeling, several crucial indicators for evaluating system reliability were derived: single-point availability, multi-point availability, reliability, and average lifetime. Finally, through numerical examples, the accuracy of the proposed model was validated, and the sensitivity of system reliability to the failure influence factors among components was analyzed.

To address the low reliability and poor applicability of existing continuous compaction detection indices, this study analyzed the relationship between energy transfer characteristics and compaction states during vibratory compaction processes. The vibration acceleration signals were processed and analyzed using time-series multi-correlation processing and Hilbert-Huang transform to extract energy features. A compaction quality evaluation index focused on the Modified Hilbert's Crest Factor (MHCF) was established.The applicability of MHCF was assessed through sensitivity and repeatability analyses combined with dynamic deformation modulus (Evd). Reliability verification tests were conducted. Experimental results show that: The correlation coefficient between MHCF and Evd exceeds 0.87, meeting engineering application standards. MHCF demonstrated high sensitivity and consistency in both coarse and fine filler compaction tests.The coefficient of variation for MHCF remained high in initial compaction stages but significantly decreased in later phases, reflecting the transition from fluctuating to stable compaction. Additionally, MHCF and Evd exhibited strong spatial distribution consistency, with MHCF showing superior detection accuracy in identifying weak compaction areas. These findings confirm the feasibility of MHCF as a reliable quality assessment index.

Noise measurements at the underwater shaking table laboratory analyzed effects of seismic simulation on equivalent A weighted sound levels and 1/3 octave band spectra under submerged and non-submerged conditions. An acoustic finite element model of the shaking table simulated vibration noise under various seismic loadings, with results validated against experimental data. The results show that a significant increase in sound pressure levels within shaking table's operating frequency range, spectrums exhibits a distinct low frequency distribution characteristic, with low frequency sound energy accounting for over 80% of the total; This low frequency acoustic environment is influenced by airborne noise from the shaking table and structural noise from vibrating components; Experimental and simulation results agree well, indicating that suppressing mid-to-high frequency components of simulated seismic motion can reduce low frequency noise effects.

Needle-free injection represents a significant advancement in drug delivery technology, yet experimental approaches struggle to directly measure interaction forces between medical jets and biological tissues. This study employs the SPH-FEM coupled algorithm to investigate interaction characteristics between medical water jets and multilayer biological tissues during injection. The reliability of the numerical model was verified through comparative analysis between single-layer skin penetration simulations and gel injection experiments. Subsequently, simulations of water jet penetration through multilayer tissues under varying jet velocities were conducted. The findings reveal that jet velocity directly influences elastic deformation and structural failure of tissues, with a critical threshold at 105 m/s. The radial deformation peak in adipose tissue increases from 0.18 mm to 0.26 mm as velocity rises from 110 m/s to 120 m/s; Meanwhile, the kinetic energy dissipation rate intensifies by 31% with velocity escalation from 105 m/s to 120 m/s, indicating significant dynamic mechanical constraints and motion retardation effects from biological tissues. Therefore, the coupling mechanism between high-speed microjets and tissues predominantly determines depth characteristics of formed cavities, which can be effectively predicted using logarithmic growth models. This research provides critical insights for optimizing needle-free injection parameters through computational biomechanics.

Signals collected by bridge health monitoring systems are often contaminated by noise, obscuring the true structural state information and posing a significant challenge to accurate health assessments. To overcome the limitations of existing denoising methods in handling complex noise, this study introduces the Crested Porcupine Optimizer (CPO) to optimize the parameters of Variational Mode Decomposition (VMD), combined with an improved wavelet threshold denoising method. The CPO algorithm, using sample entropy as the fitness function, adaptively determines the optimal VMD parameters (decomposition layers and penalty factor) for precise modal decomposition of the original signal. The resulting intrinsic mode functions (IMFs) are then screened based on variance contribution rates to retain components with true information. Subsequently, an improved wavelet threshold method is applied for secondary denoising. The effectiveness of the CPO-VMD and improved wavelet threshold approach is validated through experiments with simulated and real acceleration signals. Results show that, across various signal-to-noise ratios, this method significantly enhances signal quality, preserves useful information effectively, and demonstrates superior performance and practicality.

High-power industrial gearbox’s transmission rotors are designed to have a nearly integer multiple transmission ratio due to application requirements, which leads to rotor beat vibration problems during operation. There are researches on the beat vibration of dual rotors, but there is currently no research on the beat vibration of dual-rotor system composed of journal bearings and gears. Firstly, a two-dimensional Reynolds equation for journal bearing is established based on fluid lubrication theory and its dynamic characteristics are analyzed. Secondly, the dynamic model of nonlinear dual-rotor system supported by sliding bearings is established, by which the displacement response and beat vibration characteristics of the dual-rotor are analyzed under different speeds, different transmission loads and different dynamic loads. Finally, operation data of a gearbox containing dual-rotors of journal bearings and gears are collected, then time-domain and frequency-domain analysis are conducted and the effectiveness of different treatment measures are verified. Combining the results of model analysis dan experimental data analysis, the beat vibration mechanism of nonlinear dual-rotor system composed of journal bearings and gears was explained and the effectiveness of improvement methods to reduce beat vibration is verified. This result can be applied to controlling and optimizing the beat vibration of industrial gearboxes.

Addressing the current reliance on empirical formulas in research concerning self-excited vibration problems of ball valves in pumped storage power stations, this study first establishes the relationship between the force acting on the moving ring and the change in water pressure difference across the valve based on electrical circuit equivalent theory. Subsequently, a dynamic simulation model coupling the pressure change in the water diversion pipeline with the motion of the moving ring was constructed by considering the transmission relationships among the force on the moving ring, its motion, the leakage gap of the valve, leakage area, leakage flow rate, and the pressure in the water diversion pipeline. Next, genetic algorithms are combined with monitored self-excited vibration data to determine the parameters of the simulation model. Finally, three vibration mitigation measures are proposed and validated using the developed model, and an optimal post-failure handling procedure for ball valve self-excited vibration is established based on this. The results demonstrate that the optimized model exhibits higher consistency between simulation outcomes and actual monitoring data, verifying the effectiveness of the proposed modeling approach. The proposed measures—opening the bypass valve, engaging the maintenance seal, and disengaging the service seal—effectively suppress self-excited vibration. Furthermore, the proposed optimal handling procedure significantly reduces the time required to transition the system into a maintenance state.

To address the limitations of traditional eigenfrequency assignment methods, which rely on global models, system matrices, and complete excitation information, a method for assigning resonant or antiresonant frequencies is proposed in this paper based on a small number of local measured receptances and uniform structural modification at multiple locations. For assigning a resonant frequency, this method only requires measured receptances at modified positions, with uniform structural modifications applied at multiple positions. For antiresonant frequency assignment under multi-excitation, the method only requires the receptances at the locations of the modifications and the target response, along with the displacement responses at these locations under initial excitation. This method does not rely on global models or system matrices, nor does it require information about the excitation force. It requires less measured data and is computationally efficient. In addition, the multi-position uniform structure modification strategy effectively overcomes the limitations of the traditional single-point modelling of structural modification, and enhances the feasibility and convenience of implementation in practical engineering. Finally, the effectiveness and robustness of this proposed method are verified through numerical examples involving a simple one-dimensional spring-mass system, a three-dimensional cylindrical shell and a perforated damping rectangular plate. Additionally, the effects of measured data errors and damping characteristics on structural modification amounts and eigenfrequency assignments are analyzed in depth.

In a pressurized water transmission pipeline, the water hammer phenomenon poses a threat to the safety and stability of the water transmission project. The research on water hammer protection is of vital importance for the safe operation of the pipeline. The traditional water hammer calculation method is simple and quick, but its accuracy is not high. Combining with a supporting water transmission project of the South-to-North Water Diversion Project, a water hammer calculation method based on the ant colony algorithm is proposed. An optimized unsteady friction model is introduced, and its reliability is verified through experiments. The Bentley Hammer software is used to establish a pipeline model to conduct a hydraulic transient analysis after the pumps in the water transmission system are shut down. The results show that the unsteady friction model optimized by the ant colony algorithm can effectively improve the accuracy of water hammer calculation. The calculation accuracy of the maximum pressure head and the minimum pressure head has been improved by about 52% and 56% respectively; the protection capacity of the hydropneumatic tank increases significantly with the increase of its volume and preset pressure; the water hammer protection effect of installing hydropneumatic tanks in parallel is better than that of installing them in series. 

As a cold processing method, water jet cutting has the characteristic of no heat affected zone compared to traditional tool cutting, and it is particularly suitable for processing materials that are sensitive to high temperatures. However, experiments have found that after adding abrasive particles to form an abrasive water jet, sparks of varying quantities often occur when cutting certain metals with certain process parameters. This phenomenon inevitably restricts the application and development of abrasive water jet cutting technology in hazardous working environments such as mines. Based on the cutting mechanism of abrasive water jet and the impact thermal characteristics of metals, this paper analyzes the mechanism of sparks generated during abrasive water jet cutting of metals, and conducts experiments on abrasive water jet cutting of titanium alloy, stainless steel, carbon steel, copper alloy, and aluminum alloy. Combining theoretical analysis and experimental results, the influencing factors and critical conditions of sparks generated during abrasive water jet cutting of metals were discussed, and a mathematical prediction model was established. The research results provide theoretical guidance for the application and development of abrasive waterjet under hazardous conditions.

Due to the merits of light weight, compact size, high transmission precision and efficiency, along with a large transmission ratio, harmonic reducers have emerged as the pivotal components within high-precision apparatuses such as robots, aerospace, and medical equipment. The high-precision and high-performance functioning of harmonic reducers lays the groundwork for the precise maneuvers of mechanisms. In the event of a malfunction in the harmonic reducer, it will invariably disrupt the normal operation of the mechanism, potentially culminating in safety hazards and substantial economic setbacks. Consequently, the fault diagnosis of harmonic reducers has been drawing escalating attention. Given their distinctive structure, operational principle, and working conditions, the fault mechanisms and characteristics of harmonic reducers diverge from those of other reducers, rendering the existing diagnostic approaches for reducers inapplicable to harmonic reducers in a straightforward manner. This paper undertakes a systematic examination of the research advancements regarding the fault mechanisms, fault signal types, and fault diagnosis methods of harmonic reducers. It conducts a comprehensive summarization of the current fault diagnosis methods for harmonic reducers predicated on the fault signal types, dissects the principal challenges confronting the fault diagnosis of harmonic reducers, and puts forward the requisite research agendas, with the aspiration of furnishing a foundation and reference for the evolution and further exploration of the fault diagnosis of harmonic reducers.

In order to solve the problem that it is difficult for stacked denoising auto-encoders network struggle to accurately recognize the fault characteristics of rolling bearings under strong noise interference and variable load conditions, a rolling bearing fault diagnosis method combining feature mode decomposition with stacked denoising auto-encoders is proposed. Firstly, the signal autocorrelation function is used to improve the traditional Gini coefficient. Secondly, the parameter adaptive feature mode decomposition method is established with the improved Gini coefficient as the modal component evaluation index, and this method is used to denoise the input signal of SDAE network. Finally, the envelope spectrum of the denoised signal is input into the SDAE network to obtain the fault type diagnosis results of the rolling bearing under variable load conditions. Case analysis based on three open source datasets show that the proposed method can effectively improve the rolling bearing fault diagnosis accuracy of SDAE network. By comparing with other methods, it is verified that the proposed method has better stability and higher fault diagnosis accuracy.

To address inefficient modeling of long-term time-domain information and insufficient edge feature extraction in time-frequency images for bearing fault diagnosis, this study proposed a dual-stream method named Dynamic Edge-Contextual Aware Mamba-Transformer (DECAMambaT). This method constructed a time-domain state stream for one-dimensional time-domain information of raw vibration data. Compared with the square complexity Transformer, this method modeled the state space to form a Mamba-Transformer encoder. While accurately capturing short-range local patterns, it efficiently modeled long-range dependencies at linear complexity, effectively characterizing time-domain sensitive features. Simultaneously, a time-frequency edge stream was constructed. A two-dimensional time-frequency image of the original vibration data was obtained using wavelet transform. Compared with CNN, which only relies on fixed filters to extract edge features, a differentiable edge-sensitive mask was designed to achieve adaptive separation of impact features and background noise in this image. Additionally, a boundary-context-aware adaptive padding module was designed to maintain the topological integrity of image boundaries. These components formed a dynamic edge-contextual aware convolution for effective extraction of time-frequency edge features. Finally, unlike the fusion strategy of simply concatenating time-domain and time-frequency domain features, channel enhancement and channel gating modules were designed to establish a dynamic channel interaction gating mechanism that generates feature‑enhancement vectors and dynamic gating weights to adaptively cascade dual‑stream information, achieving a unified, bidirectionally enhanced representation between the time‑domain and time‑frequency domains, thereby significantly improving fault discrimination capability. Experiments showed that the average accuracy of this method on the CWRU and self-built experimental platform bearing dataset AUST achieved 99.87% and 99.33%, respectively, verifying its effectiveness.

In response to the challenges posed by large-scale data, multi-source noise interference, and real-time requirements in Distributed Acoustic Sensing (DAS) for conveyor belt vibration monitoring, this paper proposes a framework named variational mode decomposition-Matrix（VMD-Matrix）, which transforms the modal identification problem of mechanical vibration faults into a feature mapping and efficient retrieval problem. Firstly, Variational Mode Decomposition (VMD) is employed to adaptively extract eight physically independent Intrinsic Mode Functions (IMFs), and Gaussian fitting is applied to the power spectrum of each IMF to obtain the "amplitude–central frequency–bandwidth" triplet features. By constructing an 8×3-dimensional compressed feature matrix, the characteristics of each vibration mode are effectively compressed and represented as a low-dimensional vector, retaining key physical information such as modal energy distribution and spectral shape, while significantly reducing the data dimensionality. Subsequently, lightweight machine learning methods are integrated for rapid feature retrieval and fault diagnosis. Experimental results show that the average retrieval time is 0.8 milliseconds, with a cross-batch matching accuracy of 97.2% and a classification accuracy exceeding 95%, resulting in an approximately 80% improvement in computational efficiency. Validation in the 1 km DAS project site at Qinhuangdao Port further demonstrates that the framework enables millisecond-level real-time alarms and fault knowledge accumulation, offering high interpretability and good database compatibility. This provides an efficient and scalable solution for intelligent monitoring and equipment health management in the era of DAS big data.

To tackle the challenge of extracting weak fault information from bearings, this paper presents an enhanced filtering approach based on integrating mathematical morphology and cepstrum. To address the difficulty in determining weight coefficient for different scales in multi-scale mathematical morphology, the Particle Swarm Optimization (PSO) algorithm is adopted for parameter optimization. Firstly, a novel filtering operator is constructed, and the impact of structural element scales on the operator’s performance is analyzed through amplitude-frequency characteristic curves. Subsequently, the PSO algorithm is applied to perform adaptive filtering, the Fault Feature Energy Factor (FEF) is used for the fitness function to enable intelligent selection of weight coefficient in multi-scale morphology. Meanwhile, cepstrum is introduced to conduct secondary enhancement and extraction of fault signals. Finally, the detective efficacy of the proposed method is validated through bearing fault simulation signals and test datasets. Results indicate that the presented method demonstrates efficient capability in extracting weak bearing fault information, significantly mitigates signal noise interference, and possesses substantial practical application value.

This study integrates the theories of thermodynamics, fluid dynamics, elasticity, and rotor dynamics to establish a mathematical model of a hydrodynamic thrust bearing under impact. The finite difference method and Euler step-by-step integration method are employed to discretize the equations. A program is developed to compute the oil film pressure field, load capacity, and bearing trajectory during the impact process. Based on these calculations, the effects of various factors, such as impact amplitude, impact angle, and tilt angle, on the minimum oil film thickness, load capacity, and bearing trajectory are analyzed. The results indicate that different tilting angles have a significant impact on the minimum oil film thickness of the bearing, which in turn greatly affects the maximum oil film pressure; however, the influence on the bearing's load-carrying capacity is minimal. Under an impact load of 350 N applied vertically downward, the minimum oil film thickness decreased by 5.8%, while the load-bearing capacity increased by 9.8%. Reverse impact loads cause the bearing's characteristic parameters, such as load-bearing capacity and axial position, to exhibit an opposite trend of variation during the impact process.

To further enhance the buffering efficiency of the cushion layer on the rock sheds and take into account the resource conservation and cost reduction, the tire-derived aggregates (TDA) are introduced and mixed with gravels to form the gravel-rubber mixed cushion layer. Based on the spheropolyhedral discrete element method, the three-dimensional discrete element numerical model is established for a rockfall impacting onto the gravel-rubber mixed cushion layer. The tests of rockfall under different falling heights (2 m, 4 m, 6 m) impacting onto loose cushion layer with different TDA contents (0, 10%, 25%, 40%, 70%, 100%) are simulated, and the dynamic response of the cushion layer is analyzed and the buffering mechanism is explored. The results show that, compared with the pure gravel cushion layer, the gravel-rubber mixed cushion layer has a significantly better buffering performance. Specifically, the incorporation of TDA can reduce the peak impact force of rockfall by 18% to 73% and decrease the peak transmitted force of the cushion layer by 32% to 68%, with a more pronounced buffering enhancement under high-impact energy conditions. When the TDA content in the cushion layer is low, minimal rebound occurs during the rockfall impact process. Conversely, when the TDA content is high, a significant rebound of the rockfall along with the cushion layer is observed, and the second subsidence displacement of the rockfall after rebound mainly related to the "dynamic compaction" effect of the loose cushion layer is notably greater than the initial penetration depth (both relative to the initial elevation of the cushion layer surface). Compared with gravel particles, TDA particles have the characteristics of low stiffness, high elasticity, high friction, high damping and large aspect ratio. Hence, when the TDA content is higher, the cushion layer can reserve more contact strain energy, and the proportion of damping energy dissipation in the total energy dissipation also significantly increases. The research results provide a new perspective and theoretical basis for the optimal design of rock sheds and the improvement of the existing deteriorated cushion layers, and also provide a potential way to increase the recycling and reuse rate of waste tires.

To further investigate the mechanical properties of glued laminated bamboo (glubam) at low temperature, the impact properties of glubam with two moisture contents (oven-dry and air-dry) and two loading directions (interlayer and surface direction) at low temperatures (-196 ℃~20 ℃) and after freeze-thaw cycles were explored. The impact toughness and impact flexural strength of glubam were measured by pendulum impact tests. The results show that the impact toughness of thick-strip glubam at low temperatures shows a tendency to decrease and then increase with decreasing temperature, and its impact flexural strength is affected by loading direction. The impact toughness of thin-strip glubam at low temperature is stable and the change of its impact flexural strength is related to the moisture content. Freeze-thaw cycles weaken the impact flexural strength of glubam to different degrees, and the weakening is more obvious at ultra-low temperatures (-80℃, -196℃).

In order to study the characteristics of offshore near-fault ground motion, 49 offshore pulse-like ground motions were identified from 68,387 sets of three-component ground motion records obtained by a seafloor observation network, using wavelet analysis, energy method, and HHT method. Based on these identified offshore pulse-like ground motions, regression models for the period of offshore velocity pulses versus moment magnitude, as well as for the amplitude of offshore velocity pulses versus moment magnitude and rupture distance, were established and compared with previous onshore models. The study showed that the period of offshore velocity pulses is larger than that of onshore pulses at small magnitudes but smaller at large magnitudes. The amplitude of offshore velocity pulses is smaller than values predicted by most onshore models at small rupture distances, but exceeds predictions from most onshore models as the rupture distance increases, and decays at a slower rate. By identifying offshore pulse-like ground motions, analyzing their characteristics, and establishing the pulse parameter models, this study provides offshore-specific pulse-like ground motion input models for the seismic analysis of marine structures.

Energy dissipation damping technology is a critical measure for enhancing the seismic performance of structures and is widely applied in practical engineering. Currently, the design of energy-dissipating seismic structures generally requires extensive nonlinear time-history analysis and repeated iterations, making the design process time-consuming and labor-intensive, which severely reduces design efficiency. Given that energy-dissipating structures belong to non-proportional damping systems, this paper proposes a complex model decomposition response spectrum design method (CCQC) for their seismic design. This method is closely aligned with the design code of China and avoids the nonlinear time-history analysis used in traditional design methods, offering advantages such as fast iterative convergence and high design efficiency. The proposed design method is then applied to energy-dissipation design for a plane steel frame structure. Numerical models of the bare and damped frames are established, and nonlinear dynamic time-history analysis is performed to evaluate the effectiveness of the proposed design method. The results show that when the seismic intensity is high, significant differences in structural dynamic response are observed under different equivalent linearization methods based on CCQC calculations. The proposed design method enables the inter-story drift response of the structure to meet the code requirements and realizes the multi-level seismic performance design by achieving the performance target of "no damage in moderate earthquakes, repairable in rare earthquakes, and no collapse in extremely rare earthquakes". Compared with the bare frame, the energy dissipator significantly improves the structural seismic performance. And its yielding energy dissipation mechanism is consistent with the performance objectives, i.e., no yielding under moderate earthquakes, dissipating seismic energy during rare and extremely rare earthquakes to protect the main structure, and no failure during extremely rare earthquakes. The findings have significant implications for the design and practical application of energy-dissipating seismic structures.

In order to improve the stability of the through-the-bucket timber structure, a scheme of infilled wood shear wall between frames is proposed. Two full-scale, two-story, two-span frame specimens were designed and tested under low-cycle reversed loading. Test results demonstrated that the timber shear walls significantly improved the initial stiffness and load-bearing capacity. However, with increasing displacement, the cooperative behavior between the shear walls and mortise-tenon joints deteriorated, leading to decreased energy dissipation efficiency and accelerated stiffness degradation. Due to the tie effect of the eaves brackets, the reverse bearing capacity of the wooden shear wall wooden frame with eaves is 36.1% higher than that of the wooden shear wall filled frame. The energy consumption capacity is better, but the ductility coefficient is slightly lower. Both specimens achieved story drift ratios exceeding 1/14.5 and ductility coefficients greater than 3, indicating excellent deformation capacity. The findings provide experimental data supporting the seismic safety enhancement of traditional Chuan-Dou-style timber structures.

Vibration is one of the important factors affecting the performance of multi-rotor aircraft. Considering the vibration superposition characteristics of multi-rotor, a method based on phase angle optimization to suppress fuselage vibration of multi-rotor aircraft is proposed in this paper. This paper takes a large-sized variable pitch quadcopter unmanned aerial vehicle as the background for vibration reduction design. Firstly, the dynamic model of quadcopter UAV is constructed based on Hamilton's principle. The fuselage vibration is calculated by time finite element method and compared with test flight vibration data to verify the reliability of the model; Secondly, the Kriging surrogate model in different flight states is trained according to the phase angle and vibration simulation calculation results of four pairs of rotors. With the camera position vibration as the goal, the flight control system position and the connection position between the four auxiliary arms and the fuselage as the constraint conditions, the adaptive EI point addition criterion is adopted to improve the global calculation accuracy of the surrogate model; Finally, the optimal phase angle combination databases under different flight conditions are obtained to make the comprehensive vibration reduction efficiency of target positions reach 81.1%, 82.6%, 83.8% and 77.1% in steady level flight at 18 km/h, 36 km/h, 54 km/h and 72 km/h respectively, which proves that it is a feasible vibration suppression method. This set of methods provides new directions and possibilities for research and technological innovation in the field of multi-rotor aircraft vibration control.

An experimental study was conducted on the fatigue crack propagation process and acoustic emission (AE) signal frequency characteristics at the riveting joints of aircraft skin based on aluminum alloy lap plate. Through time-frequency analysis of the AE signals collected during the three stages of crack initiation, crack propagation, and fracture, it was determined that the characteristic frequency range of the acoustic emission signals during the fatigue crack propagation process at the riveting joints of aircraft skin is 100-175 kHz. Based on the aforementioned experimental research, this paper proposes a method for identifying fatigue cracks in aircraft skin based on the characteristic frequency of AE signals. By establishing a connection between fatigue cracks and the characteristic frequency of AE signals, accurate identification of fatigue cracks can be achieved. Additionally, this method addresses the key issue of environmental noise interference during the testing process. In subsequent fatigue tests, a method for detecting cracks in aircraft skin based on the characteristic frequency of AE signals, primarily utilizing non-destructive testing and supplemented by acoustic emission monitoring, is proposed. This method has been applied in the full-aircraft fatigue test of a cargo aircraft, demonstrating its effectiveness and providing theoretical and experimental evidence for the study of failures at riveting joints in aircraft skin.