Limited by common situations of closely spaced modes and large structural dimension, damage identification based on modal parameter is difficult to perform in civil structures. A damage identification method based on multi-level modal group response reconstruction in the presence of close spaced modes is proposed. Several modes with small intervals are grouped together, and response of the entire modal group is extracted as damage sensitive characteristic. Based on the collected modal response, a multi-level damage identification strategy is adopted. In the super element level damage location, the original structure is first converted into a super element model with fewer DOFs through model reduction, and then the minimization problem is solved by defining the modal group response strain energy as a damage index to achieve the location of damaged super elements; In element level damage identification, the minimization problem is expressed as the discrepancy between reconstructed and actual modal group response to achieve elemental damage localization and quantification. A numerical simulation study and the experimental verification were conducted to demonstrate the operational process and feasibility of this method. Compared with traditional methods, the results show that the proposed method improves the accuracy and efficiency of damage identification through multi-level identification strategies and model reduction, and on the other hand, improves the shortcomings of modal-analysis-based methods that cannot accurately identify damage when faced with close spaced modes. Regardless of the presence or absence of close spaced modes. Damage identification can be performed based on multiple dynamic responses such as stress, strain, displacement, and acceleration of the structure.

A polynomial dimensional decomposition pseudo-excitation method (PDD-PEM) was established in the frequency domain to quantify the uncertainty of random vibration power spectrum density for vehicles with uncertain parameters under random road excitation. Transforming stationary random vibration analysis into harmonic load analysis through pseudo-excitation method, and transforming double random problems into single random problems; At the same time, the polynomial dimensional decomposition method is used to construct a random Surrogate model, and the explicit function of the power spectrum density response expressed by the polynomial basis is given, which effectively realizes the probabilistic evaluation of the structural response in the uncertain parameter space. In numerical examples, the method established in this paper was used to analyze the random vibration of vehicle systems with uncertain parameters under road roughness. Compared with the Monte Carlo method, the correctness and effectiveness of the established method were verified, and the influence of uncertain parameters on the structural response statistical characteristics was further discussed. These works laid a certain foundation for considering the optimization and control problems of vehicle system parameters with uncertainty.

With the development of performance-based earthquake engineering, the ‘risk-probabilistic’ oriented performance evaluation method has gradually gained the attention of researchers, an important part of which is seismic vulnerability analysis. At this stage, there are different kinds of vulnerability methods, and more researches focus on how to combine probability theory with earthquake engineering, but the reasonable comparison for the accuracy and applicability of different methods still requires further research. Based on the nonstationary random mainshock-aftershock sequences, this paper compares three methods commonly used in seismic vulnerability at this stage: linear fitting method, maximum likelihood estimation, and Monte Carlo method. Then, based on a reinforced concrete frame, a case study is carried out, and the applicability of these three methods as well as the influence of random aftershocks are discussed. Generally speaking, the results obtained by the three methods are similar, and the development trends are relatively consistent, which also proves the effectiveness of these three methods to a certain extent. The Monte Carlo method has a long calculation period, the maximum likelihood estimation is more suitable for the performance level of minor damage, and the linear fitting method is more accurate after excluding the scattered points in the collapse state. After considering non-stationary random aftershocks, the obtained structural vulnerability shows an overall left-shifting trend. If the influence of random aftershocks is not considered, the probabilistic risk caused by earthquake sequences will be greatly underestimated.

This study extends the filtered white noise model by proposing a time-frequency hybrid dimensionality reduction model for fully nonstationary seismic ground motion random fields, thereby overcoming the limitation of simulating only ground motion processes without capturing spatially distributed ground motion fields. Specifically, to address the difficulty in directly representing the spatial coherence of seismic random fields within the impulse response function of the filtered white noise model, a proper orthogonal decomposition (POD)-based dimensionality reduction simulation method is introduced. This approach enables a frequency-domain representation of spatially coherent white noise random vector processes. By applying the impulse response functions and modulation functions corresponding to different locations within the seismic random field to filter and modulate the respective white noise components, an efficient time–frequency hybrid dimensionality reduction representation of fully nonstationary seismic random fields is achieved. Numerical examples validate the accuracy and engineering applicability of the proposed model by comparing mean values, standard deviations, auto-/cross-correlation functions, as well as response spectra and coherence functions.

A novel data-driven method for simulating non-Gaussian stochastic processes is proposed in this paper. The sample conversion model and power spectrum conversion model are established by using artificial neural network models respectively. A neural network model is constructed based on sample data to transform Gaussian samples into non-Gaussian samples. The distribution function of the samples is modeled using the shifted generalized lognormal distribution, and the latent Gaussian power spectrum is directly obtained through the backpropagation neural network model. The Gaussian stochastic process samples are generated using the spectral representation method, and then transformed into non-Gaussian process samples using the sample conversion neural network model. This method is capable of generating non-Gaussian stochastic process samples based on limited sample data, addressing the challenge of determining latent Gaussian power spectrum, and solving the problems such as poor accuracy and limited application range of the central moments-based transformation models. Through numerical simulations and validation in turbulent wind fields, the accuracy and effectiveness of the proposed method are further demonstrated.

Traditional computer vision methods usually focus on the in-plane dynamic response of structures. Therefore, this paper proposes an image phase-based stereo matching temporal analysis method to achieve targetless robust monitoring of three-dimensional structural deformation. This method uses 2D-Gabor filters and Gaussian pyramid gradient algorithms for image preprocessing, applies a phase-based dense optical flow tracking algorithm and an improved semi-global block matching (SGBM) algorithm to realize full-field measurement of structural displacement in the region of interest, and further proposes an intuitive displacement-strain conversion method to measure three-dimensional strain of structures. Through virtual reality experiments based on physics-based graphics models (PBGM), it is verified that the error of this method compared with 3D-DIC and finite element analysis deformation is less than 2%; in vibration tests of outdoor bridge structures in the laboratory, the deformation error compared with traditional testing methods can be controlled within 8%, meeting engineering application accuracy. Without compromising accuracy, this method achieves targetless robust monitoring of three-dimensional structural deformation, and better solves the problems of large environmental impact and high cost in traditional structural deformation monitoring.

Real-time hybrid test has been applied to the performance test of high-speed train running on the bridge in recent year. In order to avoid the damage of specimens or loading systems caused by instability, it is necessary to study the stability of real-time hybrid test on traveling train-bridge system. The time-varying characteristic of the traveling train-bridge system poses challenges to the stability analysis of real-time hybrid test. Therefore, it is necessary to develop suitable stability prediction methods for the time-varying system. Firstly, the time-varying discrete state space equation of the real-time hybrid test on traveling vehicle-bridge system was established, which can accurately describe the changes of all state quantities of the test system over time. Then a stability criterion based on the spectral radius of the cumulative state transition matrix was proposed, and then by combining the stability criterion with the dichotomy method, a relative stability prediction method for the time-varying real-time hybrid test was developed. A serial of practical real-time hybrid tests on traveling vehicle-bridge system was conducted based on a shaking table. The results show that the critical stability obtained by the practical tests was in good agreement with the predicted results based on the developed stability prediction method. The developed method can accurately predict the stability of RTHT on vehicle-bridge coupled system.

Environmental microvibration affects the accuracy of precision instruments, making microvibration measurement and assessment crucial. Microvibration level measurement is based on the octave spectrum of the velocity signal. However, the frequency-domain FFT method used in traditional octave analysis suffers from shortcomings such as fixed resolution and low-frequency spectrum leakage. Therefore, a complex-analysis ZFFT correction algorithm based on ratio correction was proposed. Simulations show that this algorithm improves spectral resolution while maintaining the same number of FFT analysis points. While maintaining the same number of sampling points, the computational effort is significantly reduced, and the spectrum amplitude error is as low as one thousandth. Frequency-band octave analysis is employed to suppress low-frequency spectrum leakage and increase the number of spectral lines. A microvibration monitoring and analysis system was developed, comprising a low-frequency microvibration sensor, the MI-7208 intelligent measurement device, and microvibration level measurement and assessment software. Field measurements verified the system's ability to detect VC-F-level microvibration signals and its long-term measurement stability.

The fractional nonlinear Zener model is used to describe the nonlinear and viscoelastic constitutive relation of the vibration isolation system. The variation law of system amplitude-frequency response and backbone under the combined action of constant excitation and harmonic excitation is discussed, and the influence of constant excitation on the dynamic behavior of vibration isolation system is discussed significantly. The fractional-order derivative term is made equivalent to a term in the form of trigonometric function, the steady-state response of the system is solved by harmonic balance method, and the results are compared with a variety of other methods. The influences of different parameters on the coexistence frequency band range of the amplitude-frequency response multi-state solution are summarized, and the dynamic behaviors of the system under the combined excitation are obtained by using numerically simulation. The results show that there are five solutions co-existence region in the amplitude-frequency response solution under the combined effect of constant excitation and harmonic excitation, and the system shows a phenomenon of coexistence of softening characteristic and hardening characteristic, and the backbone of the amplitude-frequency curve is tilted firstly to the left and then to the right. Additionally, it is found that the periodic motion and chaos coexist in the system under the combined excitation, and the transition laws of the polymorphic coexistence region and its adjacent regions are summarized explicitly. Affected by constant excitation, the diversity of periodic motion of the system under the combined excitation is significantly different from the dynamic behavior under the action of simple harmonic excitation alone, and the transition rules of periodic motion of the system under the action of combined excitation are summarized based on the Lyapunov exponent.

Quasi-zero-stiffness (QZS) isolators have excellent vibration isolation performance in the low-frequency range. However, in complex excitation environments, such as load mismatch condition, vibration isolation performance and corresponding stability deteriorate. To improve the vibration isolation performance of electromagnetic zero-stiffness isolators (E-QZS) and reduce the sensitivity to load, a load adaptive sliding mode control method of E-QZS is proposed. The theoretical model of an electromagnetic zero-stiffness isolator is established and a sliding mode control is designed. The range of gain coefficients for stable operation is determined using Lyapunov’s theorem. Additionally, we have devised a load-adaptive control law and conducted a corresponding stability analysis. Through simulation and experimental research, the results demonstrate that setting appropriate gains can enhance vibration isolation performance by 90%. Furthermore, the introduction of a load-adaptive sliding mode controller effectively reduces the impact of sudden load changes on isolation performance, thereby improving the robustness of the isolation system.