Goodarzi Parichehr, Heidari Farahbod, Zolotovsky Katia, Mahdavinejad Mohammadjavad
Department of Architecture, Tarbiat Modares University, Tehran, Iran.
Department of Arts and Design, Northeastern University, Boston, Massachusetts, USA.
3D Print Addit Manuf. 2025 Apr 14;12(2):192-198. doi: 10.1089/3dp.2023.0331. eCollection 2025 Apr.
Regenerative design lies on synergistic relationship between sociocultural and ecological systems, which can enable revolutionary boundaries for designing decision-making frameworks. Transitioning to regenerative design as a manifestation of systems thinking necessitates a fundamental shift from sustainable patterns and mechanistic design methodologies. At its core, regenerative design unlocks a holistic paradigm that fosters circular systems reliant on renewable resources, which can strive for equilibrium between creation and utilization. This framework goes beyond mere sustainability by actively engaging in the restoration and regeneration of its sources of energy and materials. It aspires to harness the inherent wisdom of nature, facilitating a comprehensive harmonious coexistence with environment. The integration of data-driven decision-making and regenerative paradigms can provide an insight for developing evidence-based solutions for strategic environmental and natural resource management through design practices. This short research presents a holistic data-driven and self-adaptive design strategy as the integrated problem-solver model under the imperatives of regenerative adaptive design and transfer knowledge system capable of the extensive range of applications from microscale to macroscale. The underlying idea proposes orientation on machine learning feedback loop mechanisms and nested coevolutionary loops embedded in an inclusive feedback loop frame, synergistically interfaced with the typologies of monitoring systems and intuitive datasets to problem-solve at the intersection of design, construction, and built environment. This design model can support designers, planners, and city managers in optimizing their decision-making process by relying on precise data-driven feedback in different scales of complex systems, from living bits to ecological living environments.
再生设计基于社会文化系统与生态系统之间的协同关系，这能够为设计决策框架带来变革性的突破。向作为系统思维体现的再生设计转变，需要从可持续模式和机械设计方法上进行根本性的转变。从核心来看，再生设计开启了一种整体范式，培育依赖可再生资源的循环系统，这种系统能够在创造与利用之间寻求平衡。该框架通过积极参与能源和材料来源的恢复与再生，超越了单纯的可持续性。它渴望利用自然的内在智慧，促进与环境全面和谐共存。数据驱动的决策与再生范式的整合，能够为通过设计实践制定基于证据的战略环境和自然资源管理解决方案提供见解。这项简短的研究提出了一种整体的数据驱动和自适应设计策略，作为在再生自适应设计要求下的综合问题解决模型，以及能够在从微观到宏观广泛应用范围内进行知识转移的系统。其基本理念提出了基于机器学习反馈回路机制以及嵌入包容性反馈回路框架中的嵌套协同进化回路的方向，与监测系统类型和直观数据集协同对接，以在设计、施工和建成环境的交叉点上解决问题。这种设计模型可以通过依赖不同规模复杂系统（从生物单元到生态生活环境）中精确的数据驱动反馈，支持设计师、规划师和城市管理者优化他们的决策过程。
