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• A bridge dynamic response analysis method considering the spatial dependency of uncertain parameters. • The uncertain parameter is described by a non-probabilistic interval field model. • The spatial dependency of the interval field is quantified by the Karhunen-Loève expansion. • The proposed method significantly reduces the computational effort and improves the computational efficiency. Uncertain parameters with spatial dependency exist in actual bridges inevitably, which significantly affect the bridge dynamic response. However, such spatial dependency is often neglected when investigating its influence on bridge response. This study proposes a bridge dynamic response analysis method considering the spatial dependency of uncertain parameters. Firstly, the bridge uncertain parameter is described by a non-probabilistic interval field model, and the spatial dependency between adjacent values of the interval field is quantified by the Karhunen-Loève like expansion. Thus the bridge is transformed into a system with multidimensional interval parameters by finite element method. Then, the system with multidimensional interval parameters is decomposed into several one-dimensional subsystems with only one interval parameter. Finally, the interval parameters of each one-dimensional system are divided into several subintervals with small uncertainties, and the dynamic response is obtained by combining analysis of subinterval results. Numerical examples are used to verify the accuracy and efficiency of the proposed method, and the results indicate that the proposed method significantly reduces the computational effort and improves the computational efficiency. Higher level of spatial dependency of the interval field, larger subinterval number, and lower uncertainty level of the non-probabilistic interval field leads to higher dynamic analysis accuracy. [ABSTRACT FROM AUTHOR]

• An approach is proposed for identification of bridge bending stiffness distribution using improved Gaussian peak function. • The improved Gaussian peak function allows for considering a wider range of stiffness variation models and thus is a general model. • The Levenberg-Marquardt (LM) method is proposed for parameter estimation and shows good robustness with different initial parameter values. • Numerical simulation and laboratory experiment are used to verify the effectiveness of the proposed method in identifying bridge bending stiffness distribution. Bridge damage and bending stiffness deterioration can lead to a decrease in the bearing capacity of a bridge, posing significant safety concerns. To ensure the safe operation of bridges, it is crucial to accurately identify bending stiffness deterioration and assess the bridge performance. The bridge influence line (IL), which reflects the deterioration of bending stiffness, was widely used in the assessment of bending stiffness deterioration. Existing IL-based methods mostly assume a uniform reduction in bending stiffness for identification. In reality, the deterioration of bridge bending stiffness is far more complex, which causes difficulty in accurately identifying the bending stiffness distribution using the uniform reduction model. This paper proposes a novel method for identifying bridge stiffness distribution using an improved Gaussian peak function model. The improved model incorporates additional parameters, allowing for considering a wider range of stiffness variation and thus addressing the limitations of traditional models. The parameters of the improved Gaussian peak function model are estimated by minimizing the residual between the predicted deflection influence line (DIL) and the actual DIL using the Levenberg-Marquardt (LM) algorithm. This approach enables the accurate identification of bending stiffness distribution without baseline information. Numerical simulation and laboratory experiment are used to verify the effectiveness of the proposed method in identifying bridge bending stiffness distribution. A comparison between the identified and actual bending stiffness distribution curves under various forms of damage assesses the applicability of the proposed method. [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]