Abstract
When simulating the hygrothermal behaviour of a building component, many uncertainties are involved (e.g. exterior and interior climates, material properties, configuration geometry). In contrast to a deterministic assessment, a probabilistic analysis enables including these uncertainties, and thus allows a more reliable assessment of the hygrothermal performance. This easily involves thousands of simulations, which easily becomes computationally inhibitive. To overcome this time-efficiency issue, a convolutional neural network, a type of metamodel mimicking the original model with a strongly reduced calculation time, can replace the hygrothermal model. This was proven in a previous study for a massive masonry wall, where variability of exterior and interior climate, brick material properties and wall geometry was included. However, the question rises whether it is possible to train the network on a limited number of climates, and afterwards use the network to predict accurately for other climates as well. This paper thus focuses on this aspect, and results show that, as long as the range of the new climate data falls within the range of the climate data the network was trained on, the network is able to predict accurately for new climates as well.
Highlights
Evaluating the hygrothermal performance of a building component in a probabilistic framework [1, 2] allows considering many uncertainties, such as the exterior and interior climate, the material properties, or even the configuration geometry
This paper is a first explorative study, which focuses on the case of a massive masonry wall and the possibility to use the convolutional neural networks (CNN) to extrapolate to other climates after training on a limited dataset
As the CNN can predict 256 samples in only 20 seconds, it pays off if the probabilistic assessment requires more than 1280 samples
Summary
Evaluating the hygrothermal performance of a building component in a probabilistic framework [1, 2] allows considering many uncertainties, such as the exterior and interior climate, the material properties, or even the configuration geometry. Static metamodels are developed for a specific single-valued performance indicator (e.g. the total heat loss or the maximal mould growth index), whereas dynamic metamodels aim to predict time series (temperature, relative humidity, moisture content, ...) The latter provide a more flexible approach, as predicting the hygrothermal time series allows post-processing by any desired damage prediction model (e.g. the VTT mould growth index), and provides information over the whole evaluation period. The question rises whether it is possible to train such a network on a dataset with a limited number of cases, and afterwards use it to extrapolate and accurately predict the hygrothermal performance of other wall types, materials or exterior climates This would significantly increase the metamodel’s power, as a single metamodel could be employed for a wide variety of cases without the need to increase the training dataset. This paper is a first explorative study, which focuses on the case of a massive masonry wall and the possibility to use the CNN to extrapolate to other climates after training on a limited dataset
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