ArticleBiomimetics (Basel, Switzerland)2026
Closed-Loop Human-AI Decision Support for Bio-Inspired Architectural Concept Generation.
Article in Biomimetics (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Bio-inspired architectural design increasingly relies on generative artificial intelligence to expand early-stage concept exploration, yet current workflows often suffer from vague requirement definition, subjective proposal selection, and weak connections between user expectations, visual generation, and design evaluation. This study proposes a closed-loop human-AI decision-support framework for biomimetic architectural concept generation. The framework first uses Kansei-oriented requirement analysis and the KANO model to identify and classify stakeholder expectations concerning morphology, structural rationality, environmental integration, cultural narrative, visual novelty, interactivity, and sustainability. On the basis of 282 valid questionnaire responses, the most influential requirement categories are further translated into an Analytic Hierarchy Process (AHP) hierarchy, where expert judgement from a five-member specialist panel is used to derive criterion and sub-criterion weights. These weights are then converted into structured prompts-via a formally specified weight-to-language conversion strategy-to guide a diffusion-based image-generation system toward more targeted biomimetic concepts. Finally, TOPSIS is applied to rank generated design alternatives according to the same weighted criteria, thereby creating a traceable link from requirement discovery to generation and decision-making. Case studies involving eagle-, manta ray-, and cheetah-inspired architectural concepts indicate that the framework improves the explicitness of design objectives, supports more consistent comparison among alternatives, and reduces reliance on purely intuitive aesthetic judgement. An ablation comparison confirms that each stage of the framework contributes incrementally to the quality of final outcomes. This study proposes an integrated workflow that combines requirements modeling, multi-criteria evaluation, and AI-assisted visual design.
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