Two-stage stochastic model updating method for highway bridges based on long-gauge strain sensing

Chen, Shizhi; Zhong, Qiang-Ming; Hou, Shitong; Wu, Gang

doi:10.1016/j.istruc.2022.01.082

Cited by 15 publications

(6 citation statements)

References 42 publications

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“…In addition, numerical and experimental studies showed that TL enables BMU to recognize injury severity despite modeling errors. Chen et al [140] proposed a two-stage method for bridge FEMU. The proposed method combined a radial basis function (RBF) neural network and Bayesian theory.…”

Section: Bayesian Methods-based Femumentioning

confidence: 99%

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The Current Development of Structural Health Monitoring for Bridges: A Review

et al. 2023

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The health monitoring system of a bridge is an important guarantee for the safe operation of the bridge and has always been a research hotspot in the field of civil engineering. This paper reviews the latest progressions in bridge health monitoring over the past five years. This paper is organized according to the various links of the bridge health monitoring system. Firstly, the literature on monitoring technology is divided into two categories, sensor technology and computer vision technology, for review. Secondly, based on the obtained monitoring data, the data processing methods including preprocessing, noise reduction, and reconstruction are summarized. Then, the technical literature on abnormal data early warning systems is summarized. The recent advances in vibration-based and non-destructive testing-based damage identification methods are reviewed in the next section. Finally, the advantages and disadvantages of the existing research and the future research directions are summarized. This review aims to provide a clear framework and some reliable methods for future research.

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Section: Bayesian Methods-based Femumentioning

confidence: 99%

“…Transitional Markov chain Monte Carlo sampling and fast Bayesian FFT Steel truss bridge [138] Transfer learning is used to bridge the bias Numerical and experimental models [139] Radial basis function Numerical and experimental models [140] Nonlinear Model…”

Section: Gaussian Processmentioning

confidence: 99%

The Current Development of Structural Health Monitoring for Bridges: A Review

et al. 2023

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“…This data is helpful in validating the natural frequencies and mode shapes of a bridge for various Structural Health Monitoring (SHM) purposes. The vibration data (such as acceleration) can be used together with static responses of the bridge (such as stress, strain, or displacement) to update a FE model through deterministic or stochastic methods (Chen et al 2022). A common way to validate an FE model without carrying out a complex model updating process is to identify and calibrate modal parameters of a bridge using e.g., modal identification methods and altering structural properties such as Young's modulus, material densities, boundary conditions, members or FE elements constraints/fixities through a rigorous sensitivity-based approach until the FE model represents the actual structure with a good approximation (Ghiasi et al 2022a(Ghiasi et al , 2022b.…”

Section: Nosing Load Estimationmentioning

confidence: 99%

Estimation of nosing load in existing railway transom top bridges based on field testing and finite element modelling

Ghiasi,

Lee

2024

ABEN

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A significant number of wind bracings in existing railway transom top bridges are numerically assessed deficient against the assessment nosing load recommended by the AS5100, where in almost all cases, there is no observed evidence of wind bracings being overloaded. This paper estimates the nosing load applied by various trains to a couple of random spans of an existing railway transom top bridge. Firstly, field testing of this bridge is conducted and the measured stresses at the mid-center of girders and wind bracings are collected during various normal train operations to validate the developed Finite Element (FE) models of this bridge. Then, the nosing loads due to different trains are estimated using the validated FE model through a two-staged validation approach, including automatic FE stress intensity optimization and rigorous manual FE model sensitivity analysis while transoms in various conditions are also incorporated in the FE model. Results demonstrate that the nosing load is significantly less than the required load in the AS5100 with magnitudes ranging between 8.6% to 9.4% of the maximum vertical axle load of the passed trains; suggesting that the AS5100 assessment nosing load should be revised to avoid unnecessary expensive upgrades of numerically assessed deficient wind bracings.

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“…The surrogate models commonly used in structural finite element model updating mainly include the polynomial response surface model [15][16][17], radial basis function (RBF) model [18,19], Kriging model [20], and neural network model [21][22][23][24][25]. Xu [26] proposed a bridge structure finite element model updating method based on the sparrow search algorithm (SSA) and the polynomial response surface method, and by updating the model of the high-dimensional locally damaged cantilever beam, the feasibility of the proposed method was verified.…”

Section: Introductionmentioning

confidence: 99%

Bridge Model Updating Based on Wavelet Neural Network and Wind-Driven Optimization

He,

Zeng,

Zhou

et al. 2023

Sensors

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Aging, corrosive environments, and inadequate maintenance may result in performance deterioration of civil infrastructures, and finite element model updating is a commonly employed structural health monitoring procedure in civil engineering to reflect the current situation and to ensure the safety and serviceability of structures. Using the finite element model updating process to obtain the relationship between the structural responses and updating parameters, this paper proposes a method of using the wavelet neural network (WNN) as the surrogate model combined with the wind-driven optimization (WDO) algorithm to update the structural finite element model. The method was applied to finite element model updating of a continuous beam structure of three equal spans to verify its feasibility, the results show that the WNN can reflect the nonlinear relationship between structural responses and the parameters and has an outstanding simulation performance; the WDO has an excellent ability for optimization and can effectively improve the efficiency of model updating. Finally, the method was applied to update a real bridge model, and the results show that the finite element model update based on WDO and WNN is applicable to the updating of a multi-parameter bridge model, which has practical significance in engineering and high efficiency in finite element model updating. The differences between the updated values and measured values are all within the range of 5%, while the maximum difference was reduced from −10.9% to −3.6%. The proposed finite element model updating method is applicable and practical for multi-parameter bridge model updating and has the advantages of high updating efficiency, reliability, and practical significance.

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Two-stage stochastic model updating method for highway bridges based on long-gauge strain sensing

Cited by 15 publications

References 42 publications

The Current Development of Structural Health Monitoring for Bridges: A Review

The Current Development of Structural Health Monitoring for Bridges: A Review

Estimation of nosing load in existing railway transom top bridges based on field testing and finite element modelling

Bridge Model Updating Based on Wavelet Neural Network and Wind-Driven Optimization

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