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Moisture content prediction of rain-exposed wood: Test and evaluation of a simple numerical model for durability applications

Författare

Summary, in English

Abstract in French
Decay-prediction models can be used to predict the service life of wooden components. However, knowledge of how the material climate, i.e. moisture content and material temperature, varies with time is required. A reliable material climate prediction model is therefore crucial in situations when measurements are not viable. The aim of this paper is to develop and assess the performance of a simple numerical moisture transport model for rain-exposed wood. The main focus is on the influence of rain and moisture transport in the transversal direction.
First, a model based on Fick’s second law of diffusion was calibrated against laboratory measurements where wooden boards were exposed to artificial rain. Second, the model was tested against field-test measurements on wooden boards in use-class 3.1, i.e. above-ground, exposed to rain and free to dry. The influence of rain was investigated by studying the difference between sheltered and exposed specimens over time. Finally, the model was applied to a number of Swedish climates and two different decay-prediction models were used to assess the output.
The main conclusion is that the influence of rain can be reproduced with sufficient accuracy for the particular application. The error between the numerical result and measurements tends to increase with decreasing temperature and at high moisture contents. However, the total error is reduced when the moisture content history is post-processed in a decay-prediction model as the rate of decay decreases with decreasing temperature.

Publiceringsår

2016

Språk

Engelska

Sidor

126-136

Publikation/Tidskrift/Serie

Building and Environment

Volym

97

Issue

February

Dokumenttyp

Artikel i tidskrift

Förlag

Elsevier

Ämne

  • Building Technologies

Nyckelord

  • moisture content
  • decay prediction
  • wood
  • durability

Status

Published

ISBN/ISSN/Övrigt

  • ISSN: 1873-684X