Hygrothermal assessment of timber frame walls using a convolutional neural network

Tijskens Astrid; Roels Staf; Janssen Hans

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Hygrothermal assessment of timber frame walls using a convolutional neural network

机译：使用卷积神经网络的木材框架墙的湿热评估

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A correct design of a timbre frame wall's composition is vital to avoid moisture damage. Unfortunately, currently, no general guidelines exist to determine the most optimal wall composition in a specific context. To develop such general guidelines, a comprehensive study is required, taking into account the inherent uncertainty and variability of involved input parameters. Such a probabilistic assessment is typically carried out through a Monte-Carlo approach, which easily becomes computationally inhibitive. This paper thus makes use of a metamodel, which mimics the complex hygmthermal model while being considerably faster. The authors previously developed a convolutional neural network and demonstrated its' capacity to predict the highly non-linear hygmthermal response of a massive masonry wall. In this paper, this network is adapted to predict the hygmthermal response for timber frame walls. A hyper-parameter optimisation is performed, leading to rules-of-thumb on the network architecture. It is shown that the network can accurately predict the hygrothermal time series, and that it can be employed with confidence to estimate the moisture damage risks. Subsequently, the network is used to calculate the hygrothermal response of 96 timber frame wall types, taking into account all influencing uncertainties. The results indicated that timber frame wall compositions should not be recommend based solely on the s d -ratio between vapour and wind barrier. A lower limit for the s d -ratio appears a good criterion to avoid mould growth, if adapted to the climate and cladding type. To avoid condensation, one should ensure either the insulation or the wind barrier can buffer the excess moisture.

机译：正确的Timbre框架墙的组合物设计至关重要，避免湿气损坏。不幸的是，目前，没有存在一般准则来确定特定上下文中最佳的墙壁组成。要制定这种一般指导方针，需要一项综合研究，同时考虑到所涉及的输入参数的固有的不确定性和可变性。这种概率评估通常通过Monte-Carlo方法进行，该方法容易成为计算抑制。因此，本文利用了元模型，其模仿复杂的Hygmertmerm模型，同时更快地更快。作者以前开发了一种卷积神经网络，并证明了其预测巨大砌体墙体高度非线性卫生热反应的能力。在本文中，该网络适于预测木材框架壁的卫生热响应。执行超参数优化，导致网络架构上的拇指规则。结果表明，网络可以准确地预测湿热时间序列，并且它可以充满信心地估计水分损伤风险。随后，该网络用于计算96木材框架墙类型的湿热响应，考虑到所有影响的不确定性。结果表明，木材框架壁组合物不应仅仅基于蒸汽和风屏障之间的S D -RATIO。如果适用于气候和包层类型，则S D -RATIO的下限似乎是良好的标准，以避免模具生长。为了避免冷凝，应该确保绝缘或风障可以缓冲过量的水分。

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来源
《Building and Environment》 |2021年第4期|107652.1-107652.15|共15页
作者
Tijskens Astrid; Roels Staf; Janssen Hans;
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作者单位

Katholieke Univ Leuven Dept Civil Engn Bldg Phys & Sustainable Design Kasteelpk Arenberg 40 Bus 2447 B-3001 Heverlee Belgium;

Katholieke Univ Leuven Dept Civil Engn Bldg Phys & Sustainable Design Kasteelpk Arenberg 40 Bus 2447 B-3001 Heverlee Belgium;

Katholieke Univ Leuven Dept Civil Engn Bldg Phys & Sustainable Design Kasteelpk Arenberg 40 Bus 2447 B-3001 Heverlee Belgium;

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正文语种 eng
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关键词
Metamodelling; Convolutional neural networks; Time series modelling; Probabilistic assessment; Hygrothermal assessment; Timber frame walls;

机译：Metomodelling;卷积神经网络;时间序列建模;概率评估;湿热评估;木材框架墙;

Hygrothermal assessment of timber frame walls using a convolutional neural network

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