Q-Analysis of building insulation material properties and performance for building insulation materials selection

The building sector consumes ~33% of the world's energy-related greenhouse gas (GHG) emissions. It is expected to reach ~50% in the next decade owing to the projected growth in population synergistic with change in human lifestyle propelled by technological advancement and urbanization. The building's envelope insulation is crucial for an energy-efficient and comfortable indoor environment since the envelope accounts for as high as ~60% of the total heat gain/loss in a building. We applied Q-analysis to building insulation materials properties, including, but not limited to, thermal, hygroscopic, environmental, etc., and their performance to sort and select building insulation materials―conventional, state-of-the-art, and sustainable―with optimal property combinations. Q-analysis, a popular approach for studying structural characteristics of social systems, provides an algebraic topological framework for data reduction that facilitates a macroscopic conceptualization of the system utilizing well-defined indices, viz., connectivity level, eccentricity, and complexity. The results are compared and validated with a few decision-science-driven multiple attribute decision-making (MADM) methods, including Combined compromise solution (CoCoSo), Operational competitive ratio (OCRA), Multi-attribute border approximation area (MABAC), Range of value method (ROVM), Measurement of alternatives and ranking according to compromise solution (MARCOS), and Weighted Euclidean distance-based approach (WEDBA) for evaluating consistency. Principal Component Analysis (PCA) and Hierarchical Clustering (HC) consolidated and grouped the MADM ranks of the insulation materials. The investigation highlights similarities among building insulation materials, suggests potential replacement or substitutes, and provides guidelines for designing novel building insulation materials over the current ones to push out some of the existing ones, thus reducing GHG emissions.