Abstract
Based on the basic theory of wavelet neural networks and finite element model updating method, a basic framework of damage prognosis method is proposed in this paper. Firstly, a damaged I-steel beam model testing is used to verify the feasibility and effectiveness of the proposed damage prognosis method. The results show that the predicted results of the damage prognosis method and the measured results are very well consistent, and the maximum error is less than 5%. Furthermore, Xinyihe Bridge in the Beijing-Shanghai Highway is selected as the engineering background, and the damage prognosis is conducted based on the data from the structural health monitoring system. The results show that the traffic volume will increase and seasonal differences will decrease in the next year and a half. The displacement has a slight increase and seasonal characters in the critical section of mid span, but the strain will increase distinctly. The analysis results indicate that the proposed method can be applied to the damage prognosis of girder bridge structures and has the potential for the bridge health monitoring and safety prognosis.
Highlights
Structural Health Monitoring (SHM) systems are widely used in bridge monitoring and maintenance management
A wavelet neural networks model is shown in Figure 1; it has n nodes in the input layer, l nodes in hidden layer, and b1 w1i x1
As the data become available from the SHM system, they will be used to validate and update the finite element (FE) model, and the updated and validated model can be used to develop the damage prognosis (DP) model based on the wavelet neural network method, which is the key part of the framework; the other part is damage identification, where the data of SHM system will be used to assess the current state of the structure
Summary
Structural Health Monitoring (SHM) systems are widely used in bridge monitoring and maintenance management. Physics-based models may be the most suitable approach for cost-justified applications in which accuracy outweighs most other factors and physics models remain consistent across structural systems, such as bridge structures They generally require less data than datadriven models. Data-driven approaches attempt to derive models directly from routinely collecting condition-monitored (CM) data instead of building models based on comprehensive system physics and human expertise They are built based on historical records and produce prediction outputs directly in terms of CM data [16], including the DP methods based on wavelet artificial neural network [17], Bayesian framework [18,19,20], Kalman estimator [21, 22], fuzzy theory [23], and probability analysis [24, 25]. It laid a solid foundation to the realization of the third step-predicting the remaining life of the structure
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