Long-term hygrothermal performance assessment of wood-frame walls considering climate uncertainties using partial least squares (PLS) regression

Abstract

As one of the most important input parameters for hygrothermal simulation, the climate data have a significant impact on the simulation results that are used for assessing moisture-related degradation risks of the wall. Climate change brought more uncertainties in the climate data, such uncertainties are related to different global warming (GW) scenarios in the future, and different initial conditions when projecting climate data using a meteorological model; the uncertainties of initial conditions result in different climatic realizations (runs) in each GW scenario. The objective of this work is to develop a climate ranking system concerning the mould growth risk of the wood-frame wall assemblies by considering uncertainties associated with different GW scenarios and different runs. A meta-model based on Partial Least Square (PLS) regression was developed and used as an alternative to the hygrothermal simulations for ranking the moisture severity of climatic data. The PLS model was developed for a brick cladding wall for one Canadian city, Ottawa under different levels of GW from historical (1991–2021) to future periods with an average of 3.5 °C rise in global temperature (2064–2094). The PLS model was developed using the representative climate data sets and it was further applied to the climate dataset generated over 15 runs across all GW levels to cover all the uncertainties in the projected future climates. The results showed the model can predict the mould index and can assist in ranking the runs and GW scenarios based on their moisture severity to mould growth. The ranking system will allow the researchers to assess the mould growth risk of wood-frame building envelopes during their service life without running physics-based hygrothermal simulations.