| Title |
Development of an AI-Driven Resilient Indoor Space Operations Framework for Climate and Environmental Change Adaptation |
| Authors |
장진하(Jang, Jinha) ; 임경란(Lim Kyung-ran) |
| DOI |
https://doi.org/10.14774/JKIID.2026.35.2.050 |
| Keywords |
Environmental Uncertainty; Climate Environment Change; Resilient Space Design; AI; Components |
| Abstract |
This study reconceptualizes resilience in interior space design under climate change by extending it beyond physical environmental control. While prior resilient- space approaches have largely emphasized thermal comfort, energy efficiency, and building performance, this research argues that spatial resilience must also include operational continuity and stable interaction structures among users, environments, and technological systems. Accordingly, the study aims to establish a design framework in which artificial intelligence (AI) is positioned not merely as a control/optimization tool, but as an intermediary operational layer that supports adaptive decision-making under uncertain and disruptive conditions. A qualitative multiple-case study approach is employed to analyze advanced smart building and adaptive workspace projects that respond to climate-related uncertainty. The cases include AI-enabled office environments, high-risk experimental spaces, and automated operational facilities where human?machine? environment coordination is central. Each case is examined across three dimensions: (1) functional configuration and environmental control systems, (2) hierarchical operational structures with role-based access management, and (3) interaction mechanisms that reduce conflict and sustain service continuity during disruptions.
Cross-case comparison is used to extract recurring components and synthesize them into an integrated analytical framework The findings indicate that resilient interior spaces are achieved through coordinated integration of functional systems, operational logic, and interaction structures rather than environmental performance alone. Interaction management?such as collision prevention, access control, and workflow continuity?emerges as a critical yet underexplored determinant of spatial resilience. AI is reframed as an operational mediation layer that dynamically reconfigures spatial functions and relationships as conditions change, rather than as simple automation. The proposed framework offers practical design guidelines from early design through long-term operation and provides a foundation for future empirical evaluation in climate-responsive interior architecture. |