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Neural networks for metamodelling the hygrothermal behaviour of building components

机译：使用神经网络对建筑构件的湿热行为进行元建模

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When simulating the hygrothermal behaviour of a building component, there are many inherently uncertain parameters. A probabilistic evaluation takes these uncertainties into account, allowing a more dependable assessment of the hygrothermal behaviour. However, this often necessitates many Monte Carlo simulations, which easily become computationally inhibitive. To overcome this time-expense problem, the hygrothermal model can be replaced by a metamodel, a much simpler mathematical model which aims at mimicking the original model with a strongly reduced calculation time. In this paper, a metamodel is developed to directly predict hygrothermal time series (e.g. temperature, relative humidity, moisture content), rather than single-valued derived performance indicators (e.g. maximum mould index), as these hygrothermal time series yield more information, and also allow the user to post-process the output as desired. So far, no metamodelling strategies able to tackle time series are available in the field of building physics. Because the hygrothermal response of a building component is highly non-linear and transient, this paper focuses on neural networks for time series, as they have proven successful in many other fields. The performance and training time of three popular types of networks (multilayer perceptron, recurrent neural network, convolutional neural network) is evaluated based on an application example of a massive masonry wall. The results indicate that only the recurrent and convolutional networks are able to capture the complex patterns of the hygrothermal response. Additionally, the convolutional network performed significantly better and was 10 times faster to train for the current application example, compared to the recurrent network.
机译：在模拟建筑构件的湿热行为时，存在许多固有的不确定参数。概率评估考虑了这些不确定性，从而可以更可靠地评估湿热行为。但是，这经常需要进行许多蒙特卡洛模拟，而这些模拟很容易在计算上受到抑制。为了克服这个时间问题，可以用一个元模型代替潮热模型，这是一个更简单的数学模型，旨在模仿原始模型，大大减少了计算时间。在本文中，开发了一个元模型来直接预测湿热时间序列（例如温度，相对湿度，水分含量），而不是单值导出的性能指标（例如最大霉菌指数），因为这些湿热时间序列会产生更多信息，并且还允许用户根据需要对输出进行后处理。到目前为止，在建筑物理学领域尚无能够解决时间序列的元建模策略。由于建筑组件的湿热响应是高度非线性和瞬态的，因此本文将重点放在时间序列的神经网络上，因为它们已在许多其他领域证明是成功的。基于大型砌体墙的应用示例，评估了三种流行类型的网络（多层感知器，递归神经网络，卷积神经网络）的性能和训练时间。结果表明，只有循环网络和卷积网络才能捕获湿热响应的复杂模式。此外，与循环网络相比，卷积网络的性能明显更好，并且在当前应用示例中的训练速度要快10倍。

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