The main emphasis of the MEWS project was to predict the hygrothermal responses of several wall assemblies that are exposed to North American climate loads, and a range of water leakage loads. Researchers used a method based on both laboratory experimentation and 2-D modeling with IRC's benchmarked model, hygIRC. This method introduced built-in detailing deficiencies that allowed water leakage into the stud cavity-both in the laboratory test specimens and in the virtual (modeling) " specimens "-for the purpose of investigating water entry rates into the stud cavity and the drying potential of the wall assemblies under different climate loads. Since the project was a first step in investigating a range of wall hygrothermal responses in a parametric analysis, no field study of building characteristics was performed to confirm inputs such as water entry rates and outputs such as wall response in a given climate. Rather, ranges from 'no water entry and no response' to 'too much water entry and too wet for too long' were investigated. Also, for the sake of convenience, the project used the generic cladding systems (e.g., stucco, masonry, EIFS, and wood and vinyl siding) for labeling and reporting the results on all wall assemblies examined in the study. However, when reading the MEWS publications, the reader must bear in mind that the reported results are more closely related to the nature of the deliberately introduced deficiencies (allowing wetting of the stud cavity) and the construction details of the wall systems investigated (allowing wetting/drying of the assembly) than to the generic cladding systems themselves. As a general rule, the reader must assume, unless told otherwise, that the nature of the deficiencies and the water entry rates into the stud cavity were different for each of the seventeen wall specimens tested as well as for each of the four types of wall assemblies investigated in the modeling study. For this reason, simply comparing the order of magnitude of results between different cladding systems would take the results out of context and likely lead to erroneous conclusions.
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