Abstract
In this study, a model equation is derived that uses a statistical analysis based on empirical models to predict the airtightness of reinforced concrete apartment buildings popular in Asian regions. Airtightness data from 486 units personally measured by the authors in the past eight years are used. As major variables used in the prediction model, two groups of variables are configured for the geometric components of the envelope, which is a major path of airflow in a building and is where air infiltration and leakage occur. The two groups of variables represent (1) the areas of the individual components forming the envelope and (2) the connection lengths between different components of the envelope. For the effective prediction of airtightness, correlation analysis and multiple regression analysis were applied step by step in this study. The results of the correlation analysis indicated that the areas of the slab and the window are the area variables that present the greatest impact, whereas the perimeter length of the window is the connection length variable that presents the greatest impact. Using a multiple linear regression analysis method, airtightness prediction model equations can be derived, and it is found that the model with variables for area is able to predict airtightness more accurately compared to the two models derived from variables for connection length and all variables for area and connection length. Although the statistical approach in this study shows a limitation in that the prediction results may vary depending on the attributes and type of data collected by countries, the methodology and procedure in this study contribute to similar studies for making prediction models and finding the influence of variables in the future with high applicability and feasibility.
Highlights
The airtightness of a building has a significant impact both on the level of indoor air quality and on the durability of the building envelope
This study proposes the most effective model among feasible models for predicting the airtightness of residential units based on a statistical analysis of empirical models directed at reinforced concrete apartment buildings constructed mainly in Asia
A multiple linear regression analysis is performed for major variables selected by the impact analysis, whereby an airtightness prediction model is derived that is suitable for residential units of apartment buildings constructed with reinforced concrete
Summary
The airtightness of a building has a significant impact both on the level of indoor air quality and on the durability of the building envelope. Various methods for airtightness performance prediction proposed so far in other studies have had a practical limitation in combining construction quality control and workmanship [15], which play a significant role in airtightness performance Due to this limitation, the prediction methods have not yet presented the accuracy and precision necessary to replace the experimental methodologies based on the actual on-site measurements [22]. Prignon et al [22] reviewed the predictive models and analyzed the relevance of building elements such as the location, age, and size of the building; the number of stories; and the floor area Factors related to such analyses may include energy efficiency [10]. This study proposes the most effective model among feasible models for predicting the airtightness of residential units based on a statistical analysis of empirical models directed at reinforced concrete apartment buildings constructed mainly in Asia. A multiple linear regression analysis is performed for major variables selected by the impact analysis, whereby an airtightness prediction model is derived that is suitable for residential units of apartment buildings constructed with reinforced concrete
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