Abstract Finite element model updating has become a key tool to improve the numerical modelling of existing civil engineering structures, by adjusting the numerical response to the observed experimental behaviour of the structure. At present, model updating is mostly conducted using the maximum likelihood method. Following this approach, the updating problem can be transformed into a multi-objective optimization problem. Due to the complex nonlinear behaviour of the resulting objective functions, metaheuristic optimization algorithms are normally employed to solve such optimization problem. However, and although this is nowadays a well-established technique, there are still two main drawbacks that need to be addressed for practical engineering applications, namely: (i) the high simulation time required to compute the problem; and (ii) the uncertainty associated with the selection of the best updated model among all the Pareto optimal solutions. In order to overcome these limitations, a new collaborative algorithm is proposed herein, which takes advantage of the collaborative coupling among two optimization algorithms (harmony search and active-set algorithms), a machine learning technique (artificial neural networks) and a statistical tool (principal component analysis). The implementation details of our proposal are discussed in detail throughout the paper and its performance is illustrated with a case study addressing the model updating of a real steel footbridge. Two are the main advantages of the newly proposed algorithm: (i) it leads to a clear reduction of the simulation time; and (ii) it further permits a robust selection of the best updated model.

中文翻译：

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一种改进土木工程结构有限元模型更新的协同机器学习优化算法

摘要 有限元模型更新已成为改进现有土木工程结构数值建模的关键工具，通过调整对观察到的结构实验行为的数值响应。目前，模型更新大多采用最大似然法进行。按照这种方法，更新问题可以转化为多目标优化问题。由于所得目标函数的复杂非线性行为，通常采用元启发式优化算法来解决此类优化问题。然而，尽管这在当今是一种成熟的技术，但实际工程应用中仍有两个主要缺点需要解决，即：(i) 计算问题所需的模拟时间长；(ii) 与在所有帕累托最优解中选择最佳更新模型相关的不确定性。为了克服这些限制，本文提出了一种新的协作算法，它利用了两种优化算法（和谐搜索和主动集算法）、机器学习技术（人工神经网络）和统计工具（主成分分析）。整篇论文详细讨论了我们提议的实施细节，并通过一个案例研究说明了其性能，该案例研究解决了真实钢人行桥的模型更新。新提出的算法有两个主要优点：(i) 明显减少了仿真时间；(ii) 它还允许稳健地选择最佳更新模型。