ABSTRACT
Abstract
This research examines the contributions that artificial intelligence (AI) can make to green building certification systems, with a particular focus on the evaluation and optimization of indoor environmental quality (IEQ) criteria within the Leadership in Energy and Environmental Design (LEED) framework. The study investigates the role of AI in the classification and analysis of core IEQ components, including indoor air quality, lighting efficiency, sustainable material selection, acoustic performance, and ergonomic design. The research adopts a hybrid methodological approach that combines qualitative content analysis of existing LEED-certified projects with a conceptual simulation model based on AI-supported decision-making algorithms. This model demonstrates how AI capabilities—such as real-time data processing, pattern recognition, and predictive analysis—can be integrated into the certification process. Data sources include LEED v4 documentation, AI-based environmental simulation software, and expert interviews with sustainability consultants and architects. The findings indicate that the integration of artificial intelligence can significantly enhance efficiency, objectivity, and accuracy in the assessment of IEQ criteria. AI-driven systems are shown to identify optimization opportunities within complex trade-offs between energy consumption and occupant comfort that are often overlooked by traditional manual evaluation methods. Furthermore, the study highlights AI’s potential to reduce subjective interpretations and to accelerate the overall certification process. This research contributes to the literature on AI-supported sustainable design by proposing a concrete methodological framework and demonstrating the transformative role of artificial intelligence in ecological interior architecture. The results suggest that AI-based evaluation frameworks have strong potential for standardization across green building certification systems worldwide, extending beyond LEED-specific applications.
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