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On switching response surface models, with applications to the structural health monitoring of bridges

机译：关于切换响应面模型，应用于桥梁的结构健康监测

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Structural Health Monitoring (SHM) is the engineering discipline of diagnosing damage and estimating safe remaining life for structures and systems. Often, SHM is accomplished by detecting changes in measured quantities from the structure of interest; if there are no competing explanations for the changes, one infers that they are the result of damage. If the structure of interest is subject to changes in its environmental or operational conditions, one must understand the effects of these changes in order that one does not falsely claim that damage has occurred when changes in measured quantities are observed. This problem – the problem of confounding influences – is particularly pressing for civil infrastructure where the given structure is usually openly exposed to the weather and may be subject to strongly varying operational conditions. One approach to understanding confounding influences is to construct a data-based response surface model that can represent measurement variations as a function of environmental and operational variables. The models can then be used to remove environmental and operational variations so that change detection algorithms signal the occurrence of damage alone. The current paper is concerned with such response surface models in the case of SHM of bridges. In particular, classes of response surface models that can switch discontinuously between regimes are discussed.ududRecently, it has been shown that Gaussian Process (GP) models are an effective means of developing response surface or surrogate models. However, the GP approach runs into difficulties if changes in the latent variables cause the structure of interest to abruptly switch between regimes. A good example here, which is well known in the SHM literature, is given by the Z24 Bridge in Switzerland which completely changed its dynamical behaviour when it cooled below zero degrees Celsius as the asphalt of the deck stiffened. The solution proposed here is to adopt the recently-proposed Treed Gaussian Process (TGP) model as an alternative. The approach is illustrated here on the Z24 bridge and also on data from the Tamar Bridge in the UK which shows marked switching behaviour in certain of its dynamical characteristics when its ambient wind conditions change. It is shown that treed GPs provide an effective approach to response surface modelling and that in the Tamar case, a linear model is in fact sufficient to solve the problem.
机译：结构健康监测（SHM）是诊断损坏并估算结构和系统的安全剩余寿命的工程学科。通常，SHM是通过检测目标结构中测量量的变化来完成的。如果没有竞争性的解释来说明变化，则可以推断出它们是损坏的结果。如果相关结构的环境或操作条件发生变化，则必须了解这些变化的影响，以便在观察到测量值的变化时不会错误地声称发生了损坏。这个问题-混杂影响的问题-对于民用基础设施尤为迫切，因为给定的结构通常公开暴露于天气中，并且可能会经受剧烈变化的运行条件。理解混杂影响的一种方法是构建基于数据的响应面模型，该模型可以将测量变化表示为环境和操作变量的函数。然后可以使用这些模型来消除环境和操作方面的变化，从而使变化检测算法可以单独发出损坏的信号。目前的论文涉及桥梁SHM情况下的这种响应面模型。特别是，讨论了可以在不同状态之间不连续切换的响应表面模型的类别。 ud ud最近，研究表明高斯过程（GP）模型是开发响应表面或替代模型的有效手段。但是，如果潜在变量的变化导致感兴趣的结构在方案之间突然切换，则GP方法会遇到困难。瑞士Z24桥就是一个很好的例子，这在SHM文献中是众所周知的，瑞士Z24桥随着甲板的沥青硬化而冷却到零摄氏度以下时，完全改变了它的动力特性。此处提出的解决方案是采用最近提出的树木高斯过程（TGP）模型作为替代方案。在Z24桥上以及英国Tamar桥的数据中都说明了该方法，该方法在环境风况变化时，在其某些动力特性上显示出明显的开关行为。结果表明，树状GP提供了一种有效的响应面建模方法，而在Tamar案例中，线性模型实际上足以解决该问题。

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    Worden K.; Cross E.J.;

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