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In cable-stayed bridges, cables are susceptible to fatigue damage due to cyclic traffic loads. The accurate monitoring of cable tension is critical in assessing fatigue damage and ensuring the service performance of cables. The vibration method establishes a formula between the cable tension and its vibration frequency. Therefore, the main challenge in identifying the time-varying cable tension is to determine the instantaneous frequencies (IFs) of the cable. Variational nonlinear chirp mode decomposition (VNCMD) exhibits promising advantages in identifying IFs from measured vibration signals, owing to its robustness against noise interference. However, hyperparameter selection significantly affects the IF identification accuracy of VNCMD, limiting its applicability. In this study, an improved VNCMD, termed variational nonlinear chirp mode extraction (VNCME), is proposed to address this limitation and is used to identify the time-varying cable tension. First, VNCME constructs a novel algorithm framework by modifying the optimisation function of VNCMD, eliminating the need for hyperparameter presetting and enabling the identification of a specific-order IF for the cable. The identified IF is then incorporated into the formula of the vibration method to calculate the time-varying cable tension. Examples of nonstationary signals of a sinusoidal function, finite element model of a cable, and laboratory experiments of scaled cables are used to illustrate the advantages of the proposed method, which proves to be successful in time-varying cable tension identification. In addition, the proposed method is implemented on an actual highway cable-stayed bridge to identify the time-varying cable tension over a 24-h duration. The results demonstrate that the proposed method is practical and reliable, providing a new path for the real-time monitoring of cable tension. [ABSTRACT FROM AUTHOR]

Vehicle on-board monitoring methods use responses of running vehicles to identify track irregularity in real time. But the parameters of both the bridges and the vehicles in such processes may have a non-negligible degree of uncertainty or randomness that will inevitably lead to uncertainty in the track irregularity identification. Quantifying such uncertainty is a great challenge as the vehicle-bridge system (VBS) to be treated with is time-dependent and random. This paper proposes an on-board track irregularity identification algorithm that realizes an inverse random dynamic analysis of the VBS to quantify estimation uncertainty by using a Bayesian Kalman filter technology. The proposed algorithm utilizes sigma point sets to simulate the random parameters and the noises, and is therefore capable of implementing high-fidelity probability density propagation in the nonlinear state transfer and observation functions describing the VBS. The proposed algorithm is validated by numerical examples with various running states and parameter uncertainty levels. [ABSTRACT FROM AUTHOR]

Vehicle and bridge systems (VBSs) are subjected to stochastic excitations. Significant research effort has been devoted to the stochastic response analysis of VBSs. In most previous studies, VBSs have been treated as deterministic systems with certain parameters. However, the real VBSs always have uncertain parameters, such as the mass and spring stiffness of the vehicles and elastic modulus of the bridge material, which significantly affect the response power spectrum (RPS) estimation. Moreover, the equations of motion of the VBSs are essentially second-order differential equations with time-dependent and random coefficient matrices, and the excitation-response spectral relation cannot be directly described using the frequency response functions. These properties render the available methods unsuitable for the RPS estimation of the VBSs when both structural uncertainty and stochastic excitations are considered. In the present study, a harmonic-wavelet approach is proposed to estimate the time-varying RPSs of the VBSs with random parameters. The second-order differential equations of motion of VBSs with random parameters are first transformed into linear algebraic equations with random coefficient matrices using a harmonic-wavelet analysis technique; then, the excitation-response spectral relation of VBSs is derived based on these linear algebraic equations. Finally, the RPSs of VBSs with uncertain parameters are solved using the perturbation technique. The accuracy of the proposed method was verified by comparing it with the Monte Carlo method using a numerical example of railway VBS. The numerical simulation results show that the parameter uncertainties significantly affect the RPSs of the VBSs and increase the amplitude of the time-varying RPSs. The uncertainty of vehicle stiffness was the most sensitive factor for vehicle RPSs, and the bridge elastic modulus was the most sensitive factor for the bridge RPSs. [ABSTRACT FROM AUTHOR]

• Life-cycle monitoring of high-strength bolt connections is studied using AE data. • A physics-guided deep learning framework is developed based on EWT, CNN, and GMM. • Manifold damage mechanisms overlapped among different loading stages are diagnosed. • Four loading stages are identified according to proportions of damage mechanisms. Aiming at life-cycle condition monitoring of high-strength bolt connections, a physics-guided deep learning framework integrating supervised and unsupervised learning was developed to diagnose manifold damage mechanisms and identify different loading stages using acoustic emission (AE) data. For data preprocessing, empirical wavelet transform (EWT) was introduced to provide adaptive and compact time–frequency representations of AE signals where diverse damage mechanisms behaved with various energy distributions. A convolutional neural network (CNN) model was first built to extract damage-related features from multiscale EWT matrices of AE data labeled by the loading stages I, II-III, and IV that were redefined according to their dominant damage mechanisms. Stage I (static friction) could be identified directly, which was characterized by the distinct damage mechanism of plastic deformation of asperities. After that, Gaussian mixture model (GMM) was applied to cluster the extracted feature vectors of AE data induced by multiple damage mechanisms overlapped among stages II (sliding), III (confined), and IV (failure). With the help of Bayesian information criterion (BIC), three damage mechanisms were recognized, including abrasive wear of asperities, adhesive wear of asperities, and plastic deformation and cracking of screw, which were validated by their EWT components. Finally, based on the proportions of AE data corresponding to diverse damage mechanisms, the rules were established for identifying the four loading stages of high-strength bolt connections. Experimental results demonstrated that the three-class CNN model with data labeled by the redefined loading stages achieved a higher accuracy than the four-class CNN model with data straightforwardly labeled by the original loading stages. Not only the critical damage states of static friction failure (looseness) and shear failure, but the four loading stages of high-strength bolt connections and their overlapped damage mechanisms were successfully identified by the physics-guided CNN-GMM framework. [ABSTRACT FROM AUTHOR]