Developing an integrated conceptual framework for the interaction between bionic architecture and digital twin technology

محورهای موضوعی : Space Ontology International Journal

Majid Ahmadnejad Karimi ¹ , Asem Sharbaf ²

1 - Department of Architecture, Faculty of Architecture and Urbanism, Islamic Art University of Tabriz, Tabriz
2 - Department of Architecture, Faculty of Architecture and Urbanism, Islamic Art University of Tabriz, Tabriz

تاریخ دریافت : 1404/05/13 تاریخ پذیرش : 1404/07/23

کلید واژه: Bionic Architecture, Digital Twin, Integrated Conceptual Framework, Real-time Feedback, Continuous Improvement,

چکیده مقاله :

Bionic architecture, inspired by nature’s adaptive and efficient systems, is increasingly being explored in conjunction with digital twin technology to simulate and optimize built environments before physical implementation. This emerging synergy promises enhanced architectural intelligence; however, current approaches often lack a cohesive conceptual integration between bionic principles and digital twin components. This study aims to develop an integrated conceptual framework that enables a structured interaction between bionic architecture and digital twin systems, incorporating both bioinspired and digital elements within a unified model. Adopting a descriptive–analytical methodology based on a systematic literature review and thematic synthesis, the research identifies and classifies key components from each domain—such as biomimetic algorithms, sensor feedback loops, physical–virtual entities, and data-driven control mechanisms. These elements are organized into a three-layered process diagram and synthesized into a dual digital twin model comprising a biological twin (Twin A) and an architectural twin (Twin B). The framework is grounded in two core mechanisms: real-time feedback and iterative learning, supported by the integration of reinforcement learning (RL) and transfer learning (TL) algorithms. RL enables Twin A to optimize behavioral strategies through continuous environmental interaction, while TL allows Twin B to adapt architectural responses across different scenarios without retraining. The proposed closed-loop system enhances adaptability, responsiveness, and efficiency across both physical and digital domains, thereby establishing a novel paradigm of bio-cyber-architectural cognition. This framework not only contributes a theoretical foundation for intelligent architecture but also opens pathways for empirical experimentation, advanced BIM–digital twin integration, and interdisciplinary architectural innovation.

چکیده انگلیسی:

Bionic architecture, inspired by nature’s adaptive and efficient systems, is increasingly being explored in conjunction with digital twin technology to simulate and optimize built environments before physical implementation. This emerging synergy promises enhanced architectural intelligence; however, current approaches often lack a cohesive conceptual integration between bionic principles and digital twin components. This study aims to develop an integrated conceptual framework that enables a structured interaction between bionic architecture and digital twin systems, incorporating both bioinspired and digital elements within a unified model. Adopting a descriptive–analytical methodology based on a systematic literature review and thematic synthesis, the research identifies and classifies key components from each domain—such as biomimetic algorithms, sensor feedback loops, physical–virtual entities, and data-driven control mechanisms. These elements are organized into a three-layered process diagram and synthesized into a dual digital twin model comprising a biological twin (Twin A) and an architectural twin (Twin B). The framework is grounded in two core mechanisms: real-time feedback and iterative learning, supported by the integration of reinforcement learning (RL) and transfer learning (TL) algorithms. RL enables Twin A to optimize behavioral strategies through continuous environmental interaction, while TL allows Twin B to adapt architectural responses across different scenarios without retraining. The proposed closed-loop system enhances adaptability, responsiveness, and efficiency across both physical and digital domains, thereby establishing a novel paradigm of bio-cyber-architectural cognition. This framework not only contributes a theoretical foundation for intelligent architecture but also opens pathways for empirical experimentation, advanced BIM–digital twin integration, and interdisciplinary architectural innovation.

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