Offshore multilayered soil foundations exhibit complex mechanical properties.To clarify the parametric conversion relationship between the elastic and compressive moduli of the foundation, improve foundation design accuracy, and ensure structural safety.This paper proposes a foundation parameter inversion optimisation method based on multi-objective particle swarm optimization (MOPSO).Using the root mean square of the vibration displacement at the top of the tower as an indicator, the proposed method was validated against field monitoring data, with a maximum error of less than 14%.Building on this, a study was conducted to investigate the influence of various combinations of extreme wind, wave and current loads on the dynamic response of single-bucket multi-compartment foundation.The impact of scour depth on the stability of the foundation structure was investigated.The results show that the control load at the bottom of the tower is wave load, and the control load at the top is wind load.In dynamic response analysis, the combination of the reduced wind speed and maximum wave height can be used to represent the extreme operating conditions of offshore wind turbines.As the scour depth increases, the first- and second-order natural frequencies of the wind turbine decrease.When the first-order frequency is reduced by 0.7%, foundation reinforcement and anti-scouring measures should be taken.This study provides a basis for the structural design and safety analysis of offshore wind turbine foundations.

To address the complex multimodal coupling issues in the three-degree-of-freedom (3-DOF) galloping of iced transmission conductors, this paper systematically investigates the galloping initiation mechanism under coupled vertical-lateral-torsional vibration by combining Ansys numerical simulations with wind tunnel tests, and analyzes the influence of multimodal coupling on galloping responses.First, a 3-DOF nonlinear finite element model of a two-span four-bundled iced conductor was established using Ansys software, and numerical simulations under various working conditions were conducted based on a quasi-steady aerodynamic force model.Simultaneously, wind tunnel vibration tests on sectional models were carried out to measure the wind-induced responses of conductors with crescent-shaped and D-shaped ice-accreted sections under different conditions.The galloping responses were analyzed and compared using both single-degree-of-freedom (SDOF) and multi-degree-of-freedom (MDOF) theories to elucidate the underlying complex galloping mechanisms.The results indicate that transmission line galloping exhibits significant multimodal coupling characteristics, with its initiation and suppression of galloping primarily originating from aerodynamic coupling among different modes.The classical SDOF theory, based solely on a uni-directional negative aerodynamic damping criterion, is insufficient to fully describe actual galloping behavior.Complex galloping behaviors necessitate the comprehensive consideration of multimodal coupling and aerodynamic nonlinearity.The MDOF coupling theory can accurately predict the onset conditions and dominant modes of galloping.Investigating the MDOF coupling mechanism can provide reliable theoretical support for the engineering anti-galloping design.

To investigate the synergistic optimization between rail corrugation suppression and vibration reduction performance of two-mode rail dynamic vibration absorbers, this study systematically explores the influence laws of two-mode absorbers’ parameters on the friction-induced self-excited vibration of the wheel-rail system and the forced vibration response, by comprehensively employing complex modal analysis and harmonic response analysis.Firstly, a half wheel-rail-two-mode absorbers model was established, and the influence of frequency configuration on the system’s equivalent damping ratio was revealed through complex modal analysis.Subsequently, a harmonic response analysis model was further developed to evaluate the attenuation capability of the two-mode absorbers on the rail’s first pinned-pinned vibration.The results indicate that when the two frequencies of the absorbers are respectively set to the system’s dominant friction-induced self-excited vibration frequency (405 Hz) and the rail’s first pinned-pinned resonance frequency (1 018 Hz), respectively, the system’s minimum equivalent damping ratio increases from -0.049 93 to -0.030 89, while the rail’s first pinned-pinned resonance response is attenuated by 63.6%, thereby achieving the synergistic optimization of corrugation suppression and vibration reduction performance.Increasing the absorbers’ damping can further broaden the effective bandwidth and enhance the vibration reduction effect.This study reveals the coupling influence mechanism of two-mode absorbers’ parameters on the dual performance objectives and confirms the feasibility of achieving synergistic control of dominant corrugation frequency and rail resonance through reasonable frequency matching, providing a theoretical basis for the engineering optimization design of two-mode rail dynamic vibration absorbers.

To address the slow convergence issue of traditional offline iterative hybrid testing under complex nonlinear conditions, a novel offline iterative hybrid testing method based on a two-parameter improved Newton algorithm is proposed. This method discretizes the kinematic equations using numerical integration, constructs a residual function and a two-parameter enhanced Newton iteration format, thereby transforming the structural motion equations into a problem of solving a nonlinear system through offline iteration. The full-time history of restoring force responses is measured via physical substructure loading in hybrid testing, which are then used to compute the residual function and Jacobian matrix. Finally, the overall state vector is solved using the two-parameter improved Newton iteration, yielding the complete time history of structural displacement response. Taking a single-degree-of-freedom structural system with a nonlinear velocity damper as the study case, six different scenarios are set up to compare the convergence performance of fixed-point iteration, standard Newton iteration, and the proposed two-parameter improved Newton iteration. Results show that the proposed method achieves stable convergence across all six typical cases and outperforms conventional methods in terms of displacement time history accuracy, relative root-mean-square error, and number of iterations required for convergence.

To address the vibration control problem of hydraulic pipes with nonlinear boundaries, this paper proposes a vibration suppression method utilizing an Inertial Nonlinear Energy Sink (INES). First, a coupled dynamic model of the pipe-INES system is established based on the generalized Hamilton's principle. The Galerkin method is applied to discretize the governing equations, and the steady-state response of the system is obtained by combining the harmonic balance method (HBM) with the pseudo-arc length method. The accuracy of the analytical solutions is then verified via numerical methods. Subsequently, the effects of key parameters on the vibration mitigation performance are analyzed, and the Particle Swarm Optimization (PSO) algorithm is employed to achieve parameter optimization. The results indicate that the INES can significantly suppress the vibration of hydraulic pipes. Under a specific inertance coefficient, excessive nonlinear stiffness is prone to inducing higher-order harmonic resonances. For a given nonlinear stiffness, there exists an optimal damping parameter. within a certain range, increasing both the inertance coefficient and damping enhances the vibration reduction performance. Additionally, the PSO algorithm can efficiently determine the optimal parameters of the absorber. This study provides a theoretical reference for the design and application of the INES in the pipe vibration control.

Solar panels are an important component of spacecraft, and their dynamic modeling is crucial for structural design and on-orbit performance analysis. A hinge-connected plate structure is used to simulate the deployed and locked state of solar panels and its semi-analytical dynamic model is established in this paper. The main technical points are as follows. The equivalent hinge stiffness and mass characteristics were modelled by torsion springs and thin steel sheets, orthogonal polynomials were selected as displacement functions, and the displacement continuity constraints were described using the Lagrange multiplier method. The Rayleigh-Ritz method was used to establish the dynamic equations for solving its natural frequencies and modal shapes. The results were validated through experimental tests and comparison with an ANSYS finite element model, showing maximum errors of 2.251% and 1.525% in natural frequencies, with good agreement in modal shapes. Further analysis was conducted on the effects of plate length-to-width ratio, hinge mass, and position on the system's natural characteristics. The results indicate that, as the length-to-width ratio increases, natural frequencies generally decrease except for the first mode; as hinge mass increases, natural frequencies decrease, with higher modes being more sensitive; as hinge spacing decreases, natural frequencies first increase and then decrease. The above study provides a theoretical reference for the dynamic design of solar panels.

Variable-angle fiber-reinforced composite shafts (VFCS) have significant application potential in high-performance rotor systems owing to their tailorable stiffness distribution. To address the axially nonuniform anisotropy of VFCS and the gyroscopic coupling effects under high-speed rotation, a quasi-three-dimensional semi-analytical dynamic model is developed based on the Carrera Unified Formulation. High-order cross-sectional expansion functions are employed to describe complex sectional deformation, generalized boundary conditions are introduced through the penalty function method, and the unified governing equations considering both centrifugal force and Coriolis force effects are derived based on Hamilton’s principle. The accuracy and computational efficiency of the proposed model are validated through comparisons with ANSYS finite element results and existing solutions in the literature. On this basis, the effects of nonlinear fiber paths on the natural frequencies and critical speeds of VFCS are systematically investigated, revealing the regulation mechanism of variable-angle design on the dynamic characteristics of rotating shafts. The results provide a theoretical basis for the dynamic design and stability assessment of variable-stiffness composite rotating structures.

To address the challenge of accurately capturing the vibration displacement of rotating bodies using high-speed cameras under jitter conditions, a steady-state compensation visual measurement method based on "jitter-vibration separation modeling" is proposed. Unlike traditional video stabilization methods that rely on global feature matching of adjacent frames and are highly susceptible to local motion interference, the proposed method establishes a relative displacement decoupling model between static references and moving targets. Utilizing the prior characteristic of the coexistence of a static background and moving targets within the scene, the algorithm performs synchronous tracking of video sequences by constructing a dual-branch deep learning network. Specifically, a reparameterized cross-stage context interaction module (RepCSP-CIB) is introduced to enhance multi-scale small target feature extraction, while an adaptive kernel convolution module (AKConv) is incorporated to dynamically adjust the receptive field, thereby estimating and compensating for displacement errors induced by irregular camera jitter. Benchmark experiments were conducted using data collected by eddy current displacement sensors. Quantitative results demonstrate that, compared with the baseline algorithm, the proposed method reduces the mean root-mean-square error (mRMSE) of the two categories of targets by 46.23%. Under high-frequency compound jitter conditions, the correlation coefficient (r) between the compensated vibration signal and the ground truth reaches 0.9586, and the coefficient of determination (R2) reaches 0.919. The proposed method effectively mitigates complex jitter interference, meeting the stringent accuracy requirements for measuring micro-vibration displacements in rotating machinery.

To reveal the damage mechanism and crack propagation behavior of fractured rock masses under high-pressure air fracturing, an elastoplastic analytical model of the crack tip was established by considering the influence of T-stress. The effects of different stress environments and air pressure parameters on the morphology and evolution characteristics of the plastic zone were analyzed. On this basis, high-pressure air fracturing tests were conducted under true triaxial conditions. Combined with air pressure response and acoustic emission characteristics, the processes of crack initiation, propagation, and coalescence were systematically investigated.The results show that, under the impact of high-pressure air, the crack propagation process exhibits obvious staged characteristics, and the air pressure curve can be divided into three stages: crack initiation, crack propagation, and crack coalescence. Acoustic emission signals are mainly concentrated in the crack propagation stage, and their energy evolution shows a good correspondence with crack propagation behavior.T-stress affects the morphology, deflection direction, and local damage concentration range of the crack-tip plastic zone by altering the nonsingular stress distribution characteristics at the crack tip, thereby changing the preferential crack propagation path and the damage mode of the rock mass. These results reveal the intrinsic relationship between plastic zone evolution at the crack tip and crack propagation during air fracturing, and can provide a theoretical basis for coal and rock fracturing engineering.

The rotor-bearing system of hydroelectric generating units is prone to rubbing faults under complex operating conditions, which seriously threatens the safety of the units. Shape memory alloys (SMA) variable stiffness supports provide a new approach to suppressing such vibrations by actively adjusting the dynamic characteristics of the system. However, the existing SMA-based support control for hydroelectric generating units mostly adopts fixed parameter designs. When dealing with wide-speed and complex excitation operating conditions, their vibration suppression effects are limited, and there is no systematic method for selecting the optimal parameters under different operating conditions. Therefore, this paper proposes an SMA support parameter adaptive optimisation method based on the Hybrid Grey Wolf Optimization-Particle Swarm (HGWOP) algorithm. Firstly, a coupled dynamic model of the hydroelectric generating unit rotor-SMA support with unbalanced magnetic force and rubbing excitation is established. Then, with the optimization goal of minimizing the vibration peak of the rotor over the entire speed range, the HGWOP algorithm is introduced to conduct global adaptive optimization of the key controllable parameters of the SMA support - the number of metal wires. The numerical results show that compared with the fixed wire number scheme, the optimized adaptive variable wire number strategy can reduce the maximum amplitude of the system by approximately 29.3% and compress the width of the dangerous speed range by approximately 90.7%. The research in this paper has reference significance for vibration suppression in the operation of large rotating machinery units.

Nonlinear energy sink (NES) cells suppress multi-mode vibration in continuous structures, yet the performance depends on the assembly positions and parameters. This paper optimize the positions of NES cells via sparse reconstruction and selection of master degrees of freedom. The reduced-order model with these positions as master degrees of freedom exhibits modal orthogonality and captures modal characteristics, verifying that these positions concentrate the dominant multi-mode vibration energy. On this basis, the multi-time-domain response objective function is constructed, and the artificial bee colony algorithm is applied to optimize the parameters, enhancing broadband multi-mode and multi-amplitude vibration reduction performance. The results indicate that for multi-mode vibration suppression, NES cells are assembled at the positions with high modal energy and modal orthogonality; and through the stepwise parameter optimization strategy from high-order to low-order modes, NES cells obtain different activation thresholds, thereby enhancing vibration suppression efficiency of the coupling bending-torsion. For multi-amplitude vibration suppression, NES cells are assembled at the positions with high target modal energy through the assembly strategy, and the parameter optimization strategy assigns different activation thresholds to NES cells, analogous to the multi-stable NESs, enhancing the multi-amplitude vibration suppression ability. This study provides a novel assembly and parameter optimization strategy for the passive vibration reduction of continuous structures.

Structural noise induced by rock heterogeneity often obscures ultrasonic first-arrivals, severely hindering high-precision velocity inversion and tomography. To overcome this limitation, a rock adaptive variational mode decomposition (R-AVMD) denoising method is proposed for ultrasonic A-scan signals under varying lithology conditions. Firstly, four time-frequency features—center frequency, amplitude kurtosis, root-mean-square value, and estimated signal-to-noise ratio—are extracted, and K-means clustering is employed to classify 1,536 sets of A-scan signals from six lithologies into weak and strong structural noise categories. Then, a category-specific dynamic fitness function integrating waveform correlation, permutation entropy, and kurtosis is constructed to drive the Grey Wolf Optimizer (GWO) for adaptive optimization of variational mode decomposition (VMD) parameters. Finally, the signal is decomposed using the optimized parameters, and effective mode components are selected via correlation coefficients for reconstruction. Validation on 378 measured A-scan signals from the same six lithologies shows that R-AVMD achieves SNR gains across all conditions, with a mean improvement of 9.05 dB and a maximum of 11.77 dB relative to the raw signals. After denoising, the mean absolute error of Akaike information criterion（AIC） first-arrival picking is reduced from 0.58 μs to 0.15 μs, corresponding to an estimated tomographic localization improvement of approximately 0.87-1.28 mm. The results demonstrate that R-AVMD effectively enables a fully adaptive processing pipeline from noise morphology identification to decomposition parameter optimization, providing a robust tool for ultrasonic signal denoising and high-precision first-arrival picking under complex lithological conditions.

Precision spindle bearings in CNC machine tools operate continuously under demanding conditions characterized by high rotational speed, high accuracy, and high stiffness. Under such conditions, the geometric precision, thermal behavior, and lubrication dynamics of the bearing exhibit strong nonlinear coupling. The dynamic feedback and interaction among the physical fields are the primary contributors to fluctuations in bearing dynamic performance and machining stability.The study develops a multibody dynamics model that integrates the vibration responses of the inner ring, outer ring, rolling elements, and cage. Furthermore, a method based on Gaussian random numbers is proposed to characterize roundness errors, enabling quantitative analysis of the influence of accuracy of bearing on the dynamic characteristics. To investigate the multi-field dynamic evolution mechanism of precision spindle bearings, by leveraging a bidirectional force–thermal–fluid coupling framework, the dynamic, thermal, and fluid models are deeply integrated to establish a multi-field coupled dynamics model for precision spindle bearings. A high-speed spindle bearing test rig capable of measuring vibration and temperature rise was developed. Experimental measurements of vibration characteristics and steady-state temperature responses were compared with numerical simulations, thereby validating the model’s effectiveness. Based on the established model, this study investigates the effects of rotational speed, precision grade, lubricant viscosity, and spindle ambient temperature on the multi-physical field dynamic characteristics of precision spindle bearings. The results show that excessively high rotational speed and raceway roundness errors degrade the bearing's lubrication and dynamic performance; high-viscosity lubricants exhibit stronger oil film-forming capacity but exacerbate skidding and temperature rise; while elevated ambient temperature suppresses skidding and vibration to a certain extent, it weakens the elastohydrodynamic lubrication effect and reduces the service performance of the bearing. Therefore, comprehensive regulation of these factors is essential to improve bearing performance. 

Structural vibration induced by turbulent boundary layer excitation is an important factor affecting the vibro-acoustic performance of aerospace and marine structures. Therefore, developing an efficient and accurate method for predicting vibration responses is of considerable engineering significance. Taking a typical four-edge simply supported plate as the research object, the random vibration response of the plate is predicted based on the incoherent plane wave theory. The prediction results of two wall pressure cross-spectral models, namely the Corcos model and the Mellen model, are compared, and the Corcos model is adopted as the pressure input for subsequent parametric analysis based on the comparison. On this basis, the influences of excitation sample quantity, cutoff wavenumber, free-stream velocity and plate thickness on vibration responses are separately investigated. The matching relationship between the convective wavenumber of fluctuating pressure and the structural flexural wavenumber is introduced to further reveal the underlying influence mechanisms.The results show that the response tends to converge when the number of excitation realizations is no less than 15 and the truncation wavenumber is set to twice the bending wavenumber. An increase in free-stream velocity enhances the turbulent fluctuating pressure input and shifts the wavenumber-matching frequency toward a higher frequency range. In contrast, an increase in plate thickness shifts the wavenumber-matching region toward lower frequencies and significantly reduces the displacement response. The obtained results can provide useful references for flow-induced vibration assessment and vibration-resistant design of plate-like structures.

Aiming at the concealed delamination damage that easily occurs in thin-walled lined laminated plate structures during service, this paper proposes a damage identification method based on vibration transmissibility and L1-norm regularization. Leveraging the characteristic that the transmissibility function depends solely on the intrinsic properties of the structure and is independent of external excitation, this method solves the technical problems of precisely measuring excitation forces in practical engineering.To quantify structural dynamic features, a transmissibility function of acceleration response is constructed based on the multi-degree-of-freedom dynamic equations of the laminated plate. By introducing the L1-norm to sparsify the transmissibility within specific frequency bands, a delamination damage indicator (TD) is established. Swept-frequency vibration tests were conducted on square laminated plates with predetermined concealed delamination damages at different locations and typical sizes, and acceleration responses within the 0–300 Hz frequency band were analyzed. The results show that when delamination damage is located between measurement points, the transmissibility amplitude between these points increases significantly. The constructed TD indicator can effectively identify and localize local delamination damage within the measurement area, and its distribution aligns well with the actual damage conditions. This method provides an efficient and practical new approach for the health monitoring of thin-walled lined laminated plate structures in complex service environments.

To address the practical issue of rig sticking accident caused by the inherent friction nonlinearity, significant parameter uncertainty, and insufficient control accuracy in the hydraulic-electrical system of coal mining drilling rigs, this paper proposes an improved multiple control method based on the improved LuGre friction model. First, a nonlinear dynamic model of the drilling rig’s hydraulic-electrical system is established. Given that traditional friction models cannot effectively reflect the variation of friction parameters with the working position, and tend to oscillate near zero speed, an improved LuGre friction model with a zero-speed window function and position function is constructed. Due to the complexity of the drilling rig’s working conditions and the numerous factors influencing rig sticking accident, an improved particle swarm optimization (PSO) algorithm is designed, incorporating inertia weight, adaptive learning factors, and an elite mutation mechanism, to achieve high-precision identification of the improved friction model parameters. To address issues such as parameter uncertainty, feed speed fluctuations, and load pressure shocks caused by lumped uncertainties, the control strategy introduces parameter adaptation, neural network approximation, and a defined performance function. A multiple controller is constructed based on the backstepping method, and stability proof is provided. Simulation analysis reveals that the improved LuGre friction model can effectively obtain friction model parameters that meet the accuracy requirements. Additionally, based on the improved LuGre friction model, the proposed control scheme reduces tracking errors by 44.42%, 85.87%, and 88.04% compared to three other schemes, with response speeds being nearly identical. Finally, comparison with traditional hydraulic-electrical systems shows that the proposed method increases the drilling depth by 10.18% and reduces the bit jamming rate by 30%.

To ensure the structural safety of wind turbines, this study investigates the vortex-induced vibration (VIV) of wind turbine blades. A spring-suspended sectional model, together with a built-in force balance for synchronous force and vibration measurements, was adopted to experimentally simulate the VIV of representative blade airfoils. The vibration responses of the airfoils under different test angles of attack and damping ratios were obtained, and the nonlinear vortex-induced forces (VIFs) of different airfoils were identified. The effects of the angle of attack and damping on the airfoil VIV were then analyzed, and the differences between the VIFs and conventional aerodynamic forces under different operating conditions were compared. The results show that the proposed experimental method can effectively simulate the VIV of blade airfoils and identify the time histories of nonlinear VIFs. Under a given damping ratio, the VIV characteristics of representative airfoils vary significantly with the test angle of attack and exhibit pronounced nonlinear features. Under parked conditions, the single peak value of the VIF is greater than the corresponding mean aerodynamic force. For some airfoils, the single peak value of the VIF even exceeds the mean lift and drag forces under rated operating conditions. Therefore, VIF loads should be considered in the strength and fatigue assessment of wind turbine blades.

To address the high-accuracy numerical solution of nonhomogeneous and nonlinear dynamic equations of multibody systems, a variable-accuracy direct single-step integration method, denoted as DQM45, is proposed by combining explicit prediction with the differential quadrature method (DQM). In this method, the classical fourth-order Runge-Kutta method is used to perform single-step prediction of the internal nodal states, and DQM weights are then adopted for global weighted correction. This avoids the dependence of traditional high-accuracy algorithms on matrix exponential operations and inversion of the state matrix. To accommodate different nonlinear intensities and computational requirements, a computationally efficient DQM4 scheme and a higher quadrature-accuracy DQM5 scheme are constructed. Local truncation error analysis, absolute stability region analysis, and representative linear and nonlinear dynamic examples show that the proposed method has a compact formulation and is easy to implement. Compared with commonly used fixed-step integration methods, the DQM45 framework shows certain advantages in error norms, convergence order, stability interval along the negative real axis, and dynamic response preservation capability.

To address issues such as friction, wear, and noise caused by contact in traditional rotational drive systems, a design scheme for a non-contact piezoelectric actuator is proposed. In this actuator, the near-field ultrasonic energy generated by an ultrasonic transmitter enables stable levitation of the rotor and simultaneously drives its rotation. The structural design of the core components of the piezoelectric actuator, namely the ultrasonic transmitter and the rotor, is carried out. Based on fluid dynamics theory and computational acoustics theory, an axial levitation mechanical model of the piezoelectric actuator is established to determine the near-field levitation force acting on the rotor. Furthermore, through multiphysics coupling simulation methods, the rotational motion mechanism of the rotor under the influence of the near-field acoustic field is revealed from the perspective of acoustic streaming. An experimental platform is constructed to evaluate the levitation and rotational driving performance of the rotor. The axial levitation force and rotational performance are experimentally measured to validate the theoretical model and the feasibility of the proposed structural design. The experimental results show that under the conditions of an excitation frequency of 20.21 kHz and a voltage of 250Vp-p, the steady-state levitation height of rotor can reach 103.6 μm, while the steady-state rotational speed achieves 3410rpm. The proposed non-contact piezoelectric actuator design meets the requirements of non-contact driving and system integration, and provides a novel approach for applications such as non-contact processing of semiconductor wafers and non-contact manipulation in precision optical systems.

An engineering model was developed to investigate transient vibrational energy transmission in ultra-deep water pile hammer under pile refusal conditions. A structural sound intensity-based program was developed to visualize time-domain vibration energy flow in the anvil-primary damping structure..Results reveal convective patterns and local energy concentration in the anvil. Mechanical energy in the primary laminated rubber isolator shows nonlinear attenuation: amplitude decreases from lower to upper layers; peak arrival times exhibit sequential delay. These features indicate a cascaded energy dissipation mechanism.Principal propagation paths were identified, along with key energy source and convergence zones. The findings offer practical guidance for vibration control and structural optimization of pile hammer.

Concrete slabs, as common structural components, may experience blast loads during their service life. To investigate the blast resistance of steel fiber-reinforced high-strength concrete (SFHSRC) back-free slabs under near-field underwater explosions, two slab specimens were fabricated and subjected to underwater explosion tests. The experimental results showed that under non-contact explosion loading, both high-strength concrete (HSRC) and SFRHSC slabs primarily exhibited spalling failure on the blast-facing surface and bending damage on the explosion-facing surface, with crack propagation observed on the blast-facing surface. Additionally, the inclusion of steel fibers significantly enhanced the blast resistance of the high-strength concrete slabs. To determine the optimal steel fiber parameters, a validated Arbitrary Lagrangian-Eulerian (ALE) algorithm was used to study the effects of steel fiber content and aspect ratio on the dynamic response and damage distribution of the slabs. The results indicated that, for SFHSRC slabs, damage and displacement initially decreased and then increased with higher fiber content, with the optimal blast resistance achieved at a fiber volume fraction of 1.5% and an aspect ratio of 75 under non-contact underwater explosions.

Aiming at the substantial non-stationary and transient impact loads generated by oil drilling rigs under complex operating conditions such as drilling and tripping, accurate identification of shock features and probabilistic characterization of the correlation between amplitude and duration are of critical importance for condition monitoring and dynamic performance evaluation. This paper proposes a hybrid feature extraction method that integrates Ensemble Empirical Mode Decomposition (EEMD), Empirical Wavelet Transform (EWT), and the Teager Energy Operator, and further develops a joint probability model by combining nonparametric marginal distributions with Copula theory. Specifically, impact-rich intrinsic mode functions (IMFs) are reconstructed through EEMD decomposition and selection, then enhanced via EWT following high-pass filtering. The Teager Energy Operator, coupled with an adaptive threshold, is employed to detect shock peaks and extract the corresponding amplitude–duration pairs, which are subsequently used to construct the joint probability model, with goodness-of-fit comparisons conducted among various Copula families. Validation using measured power data from an oil drilling rig in an oilfield shows that the proposed method can effectively detect shock events in the signals. The constructed joint probability model exhibits good agreement with the empirical distribution and accurately captures the dependence structure between impact amplitude and duration, thereby providing an effective avenue for non-stationary feature extraction and statistical modeling of impact loads on drilling rigs.

Addressing the issue of insufficient energy absorption performance in traditional chiral structures, a design approach combining chiral and symmetric structures was proposed. Through topological optimization, an enhanced negative Poisson's ratio structure was obtained. Dynamic impact simulations were conducted on both chiral and enhanced structures, and the results indicated that the enhanced structure exhibited superior impact resistance and energy absorption characteristics. Under the premise of lightweight design, two types of core layers were used to replace the reinforcing ribs at the bottom of vehicle power battery boxes, and simulation analysis was conducted on three battery box protection models under road foreign object impact conditions. The results showed that both core layer configurations provided better protection for the battery box than the reinforcing ribs, with the enhanced negative Poisson's ratio core layer demonstrating the best performance. The peak axial compression of the battery cell closest to the impact center of the battery box was reduced by 17.6%.

To address data imbalance and misdiagnosis in rolling bearing fault diagnosis, a method based on a multi-modal parallel neural network and Progressive Focal Loss was proposed. First, a Source Pulse Enhancement Module (SPEM) was constructed to adaptively enhance original time-domain signals, and multi-modal inputs were established combined with Continuous Wavelet Transform to improve fault identifiability. Second, a parallel architecture of improved Scale-Fusion 1D-CNN and ResNet18 was adopted to extract multi-modal features. A Bidirectional Cross Attention Mechanism was utilized to realize deep fusion of inter-modal information for extracting discriminative features. Finally, a PFL function was applied to adaptively adjust attention weights of minority class samples during training. Experimental results demonstrate that under an imbalance ratio of 20, the proposed model achieves accuracies of 98.89% (CWRU) and 98.9% (JNU), surpassing various mainstream methods.

To improve the accuracy and robustness of fault diagnosis for hydroelectric generating units under non-stationary vibration signal condition, a fault diagnosis model based on dilated convolution and cross-scale attention fusion network (DCSAF-Net) is proposed. First, a multi-scale dilated convolution block is designed to capture long-range signal features without increasing parameters. Second, a bidirectional long short-term memory （BiLSTM） global attention and cross-scale fusion module is introduced to combine global and local features, which helps enhance weak fault signals. Finally, a squeeze-and-excitation （SE） channel attention mechanism is used to reduce noise and highlight key fault features. Experiments on public bearing and real-world hydropower datasets demonstrate the model's outstanding performance. Notably, DCSAF-Net achieves 99.93% diagnostic accuracy on the MFPT dataset under standard conditions. Furthermore, it maintains robust noise resistance and strong few-shot generalization under severe conditions like strong noise and limited samples. These results prove that DCSAF-Net effectively enhances fault diagnosis accuracy, providing reliable technical support for the intelligent health management of hydroelectric generating units.

As a core component of harmonic reducers, flexible thin-walled bearings undergo forced deformation of their inner and outer rings during operation, and the resulting series of impacts may mask the fault frequencies with time-varying characteristics, posing certain challenges to the time-frequency analysis of fault features. An adaptive-order local time-reassigned synchrosqueezing transform is proposed, and, based on the analysis of the estimation error of the conventional first-order group delay, a theoretical residual index and a second-order group delay stability criterion are constructed to realize adaptive switching between first-order and second-order group delay estimation. Furthermore, considering the particularity of flexible thin-walled bearing faults, a fault feature extraction method guided by optimized multipoint kurtosis is designed. Experimental results show that the proposed method can accurately extract the time-frequency features of flexible thin-walled bearing faults, thereby verifying the periodic time-varying characteristics of their fault characteristic frequencies.

Multimodal deep learning methods are gaining increasing attention in the field of rotating machinery fault diagnosis. However, existing methods, while employing multiple sensors, focus only on temporal dependencies and neglect the spatial topological relationships among sensors. Furthermore, their diagnostic accuracy and robustness in noisy environments still need improvement. To address these issues, a multi-source graph spatiotemporal collaborative diagnostic framework is proposed, comprising two modules: Multi-Source Adaptive Graph Construction (MSAGC) and Attention-Enhanced Spatio-Temporal Graph Convolutional Network (AESTGCN). First, the MSAGC algorithm is designed to construct a k-nearest neighbors graph, a minimum spanning tree, and a threshold-based cosine similarity graph. Given that a single graph structure tends to suffer from adjacency shifts in strong-noise environments, a node-level gated fusion mechanism is introduced, which enables each sensor node to adaptively learn preference weights for the three graph structures, thereby constructing a sensor network topology that is robust against noise. Second, the AESTGCN model is proposed, which extracts the temporal dependencies and spatial topological features of sensor signals by alternately stacking one-dimensional convolutional layers and graph convolutional layers, and introduces an edge attention mechanism to enhance the representational ability of key connections. Experimental results on the Southeast University and Taiyuan University of Science and Technology datasets show that the proposed method achieves accuracies of 99.87% and 98.21%, respectively, significantly outperforming existing deep learning models. Even in noisy environments, the method maintains high diagnostic accuracy, demonstrating robustness to noise.

To address the insufficient modeling of joint dependencies among features from time, frequency, and time-frequency domains in multi-domain bearing fault diagnosis, along with the limitation of recalibration-based fusion methods that often generate weights from single-domain statistics, this paper proposes the tri-domain gated interaction attention network (TGIA-Net). The method takes 75-dimensional handcrafted features as a unified input. Its core TGIA module adaptively generates a gating weight for each domain by leveraging features from the other two domains, with a Transformer then performing feature refinement and classification. In repeated experiments on the public HIT, CWRU, and MFPT datasets, TGIA-Net achieved 90.17%±0.32% accuracy on the most challenging HIT dataset, outperforming ConcatMLP by 2.17 percentage points (p<0.0001). On the other two near-saturated datasets, its performance was comparable to simple baselines, which delineates the method's applicable boundaries. The results demonstrate that TGIA-Net (with approx. 71K-155K parameters) provides a structurally clear, reproducible, and lightweight modeling framework for fusing three-domain features from co-located signals, offering practical reference value for fusion architecture design and multi-condition bearing fault diagnosis.

To effectively utilize a large number of Marine ground motion records and fully understand the characteristics of offshore ground motions, this paper proposes the concept of a bi-normalized input energy spectrum of offshore ground motions. Firstly, the differences between the bi-normalized input energy spectrum and the traditional input energy spectrum in characterizing the potential destructive potential of offshore ground motions were analyzed. Secondly, based on the statistics of a large number of offshore ground motions from the Sagami Bay area of Japan, the influence laws of seafloor stations, moment magnitude, epicentral distance, ground motion duration, postyield stiffness ratio and hysteretic behavior on the bi-normalized input energy spectrum of offshore ground motions were discussed. Meanwhile, the differences in the statistical characteristics of the bi-normalized input energy spectrum under offshore and onshore ground motions were compared. Finally, the prediction equation of bi-normalized input energy spectrum of offshore ground motions was constructed through regression analysis. The results show that compared with the traditional input energy spectrum, the bi-normalized input energy spectrum of offshore ground motions simultaneously eliminates the influence of ground motion intensity and predominant period on the spectral shape, and thus has outstanding advantages in characterizing the consistent characteristics of offshore ground motions. The bi-normalized input energy spectrum of offshore ground motions is insensitive to seafloor stations, moment magnitude, epicentral distance and ground motion duration, which is conducive to establishing a uniform input energy design spectrum model applicable to various marine environments.

An inherent trade-off exists between the isolation layer displacement and the superstructure responses in base-isolated structures. Conventional measures, such as adjusting isolation stiffness or damping, often fail to achieve simultaneous control of both objectives. Consequently, tuned viscous mass dampers (TVMD) and tuned inerter dampers (TID) have been integrated into isolated structures, demonstrating significant potential. However, research on the seismic performance of these inerter systems under near-fault pulse-like ground motions remains insufficient. In this study, standardized equations of motion for base-isolated structures with various inerter configurations are first established. Subsequently, 100 representative near-fault pulse-like earthquake records are selected as seismic inputs. To enhance computational efficiency, a Deep Neural Network is employed to construct a surrogate model for dynamic response analysis. Based on this, a multi-objective optimization framework using the NSGA-II algorithm is proposed, targeting both isolation displacement and absolute acceleration. Finally, the evolution of optimal parameters and mitigation effects is systematically analyzed across various isolation periods and structural damping ratios. The results indicate that under near-fault pulse-like earthquakes, the optimal frequency ratio is largely independent of the isolation period and damping ratio, consistently approaching the predefined upper boundary. This differs significantly from the results of traditional methods, which typically yield values near 1.0. The optimal damping ratio of TVMD decreases with increasing isolation periods and structural damping, while TID exhibits the opposite trend. Furthermore, the combined TVMD-TID configuration achieves the highest performance, with reduction ratios of 20%–24% for isolation displacement and 12%–18% for absolute acceleration.