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Stochastic Modelling of Hygrothermal Performance of Highly Insulated Wood Framed Envelopes


Stochastic Modelling of Hygrothermal Performance of Highly Insulated Wood Framed Envelopes

Wang, Lin (2018) Stochastic Modelling of Hygrothermal Performance of Highly Insulated Wood Framed Envelopes. PhD thesis, Concordia University.

Text (application/pdf)
Wang_PhD_F2018.pdf - Accepted Version


Wood-framed construction is one of the main building types for residential buildings in North America because of their features such as light-weight, easily built and environmental friendly. However, prolonged exposure to moisture during construction and in service is a durability concern for wood framed envelopes. As building energy consumption has gained increasing attention in recent years, the majority of building codes in North America require higher insulation levels in building envelopes to improve the building energy efficiency. However, the highly insulated wood framed envelopes may have higher risk of moisture problems such as mold growth and wood decay depending on their configurations. Hygrothermal simulation programs have been widely used for evaluating hygrothermal performance of wood framed envelopes. However, the uncertainties of the input parameters may result in a discrepancy between simulation results and the real performance of the wood framed envelopes, thereafter, unable to reveal the actual risks of moisture problems. Stochastic modelling has been used to investigate the uncertainties of the input parameters and their influence, however, the stochastic parameters were only limited to material properties and boundary conditions in previous studies without considering the moisture loads such as air leakage and rain leakage.

This thesis focuses on developing a methodology to evaluate the hygrothermal performance of wood framed envelopes under various moisture loads using stochastic approach. A stochastic modelling framework is developed based on a well-developed hygrothermal simulation program- DELPHIN and a robust programming platform- MATLAB. Latin Hypercube Sampling technique and Factorial Design Experiment are combined to organize the stochastic material properties, boundary conditions and moisture loads, and generate stochastic models. Uncertainty and sensitivity analysis are performed based on the stochastic input parameters and results to evaluate the moisture content level and mold growth risk, as well as the sensitivity of the moisture performance to each influential factor.

The developed stochastic modelling framework is applied to analyze the hygrothermal performance of Cross Laminated Timber (CLT) wall assemblies and compared with parametric study to demonstrate the advantages of stochastic approach. Then, the hygrothermal performance of the highly insulated wood framed walls (deep cavity walls and exterior insulated walls) are analyzed using the stochastic modelling framework. It is found that the exterior insulated walls have lower mold growth risk than deep cavity walls, and the wall with high permeance exterior insulation (mineral wool) is safer than that with low permeance exterior insulation (polyisocyanurate) in terms of mold growth. The moisture performance of the walls is more sensitive to moisture loads than to material properties, and the significance of the moisture loads (air leakage and rain leakage) depends on climate condition. The thresholds of air leakage rate and rain deposition factor are obtained for the highly insulated wood framed walls to avoid mold growth risk. The design guidelines are formulated for energy efficient and durable wood framed envelopes. The developed stochastic modelling framework can be also applied to other moisture damage risk analysis such as wood decay and the damage caused by freeze/thaw cycles.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Building, Civil and Environmental Engineering
Item Type:Thesis (PhD)
Authors:Wang, Lin
Institution:Concordia University
Degree Name:Ph. D.
Program:Building Engineering
Date:May 2018
Thesis Supervisor(s):Ge, Hua
ID Code:983994
Deposited By: LIN WANG
Deposited On:31 Oct 2018 16:48
Last Modified:31 Oct 2018 16:48
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