English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Performing numerous simulations of a building component, for example to assess its hygrothermal performance with consideration of multiple uncertain input parameters, can easily become computationally inhibitive. To solve this issue, the hygrothermal model can be replaced by a metamodel, a much simpler mathematical model which mimics the original model with a strongly reduced calculation time. In this paper, convolutional neural networks predicting the hygrothermal time series (e.g., temperature, relative humidity, moisture content) are used to that aim. A strategy is presented to optimise the networks’ hyper-parameters, using the Grey-Wolf Optimiser algorithm. Based on this optimisation, some hyper-parameters were found to have a significant impact on the prediction performance, whereas others were less important. In this paper, this approach is applied to the hygrothermal response of a massive masonry wall, for which the prediction performance and the training time were evaluated. The outcomes show that, with well-tuned hyper-parameter settings, convolutional neural networks are able to capture the complex patterns of the hygrothermal response accurately and are thus well-suited to replace time-consuming standard hygrothermal models.

Optimising Convolutional Neural Networks to Predict the Hygrothermal Performance of Building Components