Abstract

In finite-element model updating, numerical models are calibrated in order to better mimic the real behaviour of structures. Such updating process is usually performed under the maximum likelihood method in practical engineering applications. According to this, the updating problem is transformed into an optimization problem. The objective function of this problem is usually defined in terms of the relative differences between the numerical and the experimental modal properties of the structure. To this aim, either (1) a single-objective or (2) a multi-objective approach may be adopted. Due to the complexity of the problem, global optimizers are usually considered for its solution. Among these algorithms, nature-inspired computational algorithms have been widely employed. Nevertheless, such model updating approach presents two main limitations: (1) a clear dependence between the updated model and the objective function considered; and (2) a high computational cost. In order to overcome these drawbacks, a detailed study has been performed herein both to establish the most adequate objective function to tackle the problem and to further assist in the selection of the most efficient computational algorithm among several well-known ones. For this purpose, a laboratory footbridge has been considered as benchmark to conduct the updating process under different scenarios.

摘要

在有限元模型更新中，数值模型被校准以更好地模拟结构的真实行为。在实际工程应用中，这种更新过程通常是在最大似然法下进行的。据此，更新问题转化为优化问题。这个问题的目标函数通常是根据结构的数值和实验模态特性之间的相对差异来定义的。为此，可以采用 (1) 单一目标或 (2) 多目标方法。由于问题的复杂性，通常会考虑全局优化器来解决它。在这些算法中，受自然启发的计算算法已被广泛采用。然而，这种模型更新方法存在两个主要限制：(1) 更新后的模型与所考虑的目标函数之间的明显依赖关系；(2) 计算成本高。为了克服这些缺点，本文进行了详细研究，以建立最合适的目标函数来解决问题，并进一步协助在几个众所周知的计算算法中选择最有效的计算算法。为此，实验室的行人天桥已被视为基准，以在不同情况下进行更新过程。这里进行了详细的研究，以建立最合适的目标函数来解决问题，并进一步协助在几个众所周知的计算算法中选择最有效的计算算法。为此，实验室的行人天桥已被视为基准，以在不同情况下进行更新过程。这里进行了详细的研究，以建立最合适的目标函数来解决问题，并进一步帮助在几个众所周知的算法中选择最有效的计算算法。为此，实验室的行人天桥已被视为基准，以在不同情况下进行更新过程。