Deepening Layers of Urban Space: A Scenario-Based Approach with Artificial Intelligence for the Effective and Sustainable Use of Underground Parking Structures

This study proposes a scenario-based conceptual model for transforming underground parking structures into sustainable interior green spaces, directly addressing two core research dimensions: energy efficiency and user experience. The originality of the research lies in repositioning subterranean spaces-often overlooked in urban planning-as climate-responsive, multi-functional public environments. Using a site-specific case in downtown Rize, T & uuml;rkiye, three design scenarios-passive green walls, active modular systems, and experimental micro-farming-were comparatively analyzed. These scenarios were assessed through AI-assisted simulations and climate-based performance evaluations in terms of environmental benefits, thermal regulation, carbon reduction, and experiential quality. Underground space leads to green design interventions, which in turn generate environmental, energy, and social benefits. The results demonstrate that passive systems provide cost-effective improvements, active modular systems achieve balanced performance, and experimental micro-farming yields the highest ecological and social benefits. The study uniquely contributes to urban sustainable design by integrating climate-adaptive strategies, biophilic design principles, and AI-supported visualization into the transformation of underground structures. This research not only advances academic discourse but also provides policy-relevant insights for local governments, developers, and communities in the context of urban renewal.