No abstract is provided for this article.
No abstract is provided for this article.
No abstract is provided for this article.
No abstract is provided for this article.
No abstract is provided for this article.
Damage qualification is generally of concern in vibration-based damage assessment of bridges and structures, in which the model-based method, such as the finite-element (FE) model updating, is often a favorable choice in terms of efficacy and accuracy. However, the numerical problems to be solved are often ill-conditioned and sometimes even ill-posed, due to the limited number of the measurements and the large number of potential parameters to be updated in the FE model. Furthermore, in real-world applications, the effects of experimental and modeling errors. need to be accounted for From the practical point of view, there is often a trade-off between the cost of the vibration measurements and the precision of the experimental results, and another trade-off between the computational efficiency of the simplified FE model and the numerical fidelity of the refined FE model. The satisfactory solution of these problems is crucial to the success of the damage assessment in real-world applications, which remains scarce. In this paper, a multistage model updating approach is proposed for damage quantification of a prestressed concrete girder bridge by using the results of an extensive experimental campaign. Although severe corrosion damage had been found by visual inspection before the measurements, the vibration-based damage assessment was performed without a priori knowledge of the existence and location of damage. The appealing feature of this work is the scale and complexity of the studied structure. By updating a simplified orthotropic plate model, the damage was satisfactorily identified in terms of its extent and severity for both a simulated damage scenario and the real structure. A good balance between the accuracy and efficiency has been reached by the proposed multistage model updating approach. The developed methodology can be applied to similar multigirder bridges, which are common in the regional highway network.
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No abstract is provided for this article.
No abstract is provided for this article.
Algorithms for structural identification and damage detection of steel-concrete composite bridges
Finite element (FE) model updating is a technique that is commonly used for structural damage identification and localization. In FE model updating, the objective is to adjust parameters of a FE model such that its output corresponds better with experimental observations of the structural behaviour. In a structural mechanics context, the experimental data in many cases consists of modal characteristics of several natural modes. When FE model updating is applied for damage assessment, it is often assumed that the damage can be identified as a decrease in structural stiffness; using the available observations, some (substructure) stiffness parameters are updated to detect, localise and quantify structural damage. The FE model updating process involves solving an inverse problem and is subject to measurement and modelling errors, which give rise to errors/uncertainties in the predictions that are made by the FE model. The objective of this research is to quantify the effects of errors/uncertainties on the results of the damage assessment. To this end, two alternative approaches are employed: a non-probabilistic interval-based approach and a probabilistic Bayesian approach. Both methods are elaborated and applied to the damage assessment of a reinforced concrete beam.
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This paper proposes a novel approach to model updating for a large-scale cable-stayed bridge based on ambient vibration tests coupled with a hybrid metaheuristic search algorithm. Vibration measurements are carried out under excitation sources of passing vehicles and wind. Based on the measured structural dynamic characteristics, a finite element (FE) model is updated. For long-span bridges, ambient vibration test (AVT) is the most effective vibration testing technique because ambient excitation is freely available, whereas a forced vibration test (FVT) requires considerable efforts to install actuators such as shakers to produce measurable responses. Particle swarm optimization (PSO) is a famous metaheuristic algorithm applied successfully in numerous fields over the last decades. However, PSO has big drawbacks that may decrease its efficiency in tackling the optimization problems. A possible drawback of PSO is premature convergence leading to low convergence level, particularly in complicated multi-peak search issues. On the other hand, PSO not only depends crucially on the quality of initial populations, but also it is impossible to improve the quality of new generations. If the positions of initial particles are far from the global best, it may be difficult to seek the best solution. To overcome the drawbacks of PSO, we propose a hybrid algorithm combining GA with an improved PSO (HGAIPSO). Two striking characteristics of HGAIPSO are briefly described as follows: (1) because of possessing crossover and mutation operators, GA is applied to generate the initial elite populations and (2) those populations are then employed to seek the best solution based on the global search capacity of IPSO that can tackle the problem of premature convergence of PSO. The results show that HGAIPSO not only identifies uncertain parameters of the considered bridge accurately, but also outperforms than PSO, improved PSO (IPSO), and a combination of GA and PSO (HGAPSO) in terms of convergence level and accuracy.
