This decision-support platform of interior material performance characteristics moderates risk in multiple environments, ranging from housing, hospitality to hospital care and more. The built environment lacks a comprehensive, data-driven approach to selecting and implementing resilient materials able to withstand both known and unforeseen hazards while promoting human health and well-being. Traditional methods of material selection often fail to account for the complex interplay of factors affecting material performance under various stress conditions and environmental hazards, leading to potentially vulnerable structures and interiors. There is a need for an advanced computational system that can accurately predict material resilience, guide informed decision-making in material selection, and ultimately enhance the overall resilience and health-promoting qualities of built environments.
Researchers at the University of Florida have developed a decision-support platform of interior material performance characteristics related to improving material resilience. The platform allows users to use deep learning tools to extract material performance data that informs design decisions to improve material resilience.
This decision-support tool informs design decisions related to material choice to mitigate infection risks in the built environment
This decision-support platform of interior material performance characteristics informs design decisions to mitigate risks in the built environment. The platform supports the use of artificial intelligence technologies including machine learning and data mining to reveal common patterns in interior material performance used for designing resilient environments. The platform can quickly provide material performance data through scenario analysis. Interior material performance data are based on a hierarchy of technical performance measures, including robustness, recovery, graceful extensibility, and sustained adaptability.
