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认知巢:从生物学到生态的再生设计中的嵌套数据驱动的决策支持系统
Parichehr Goodarzi1, Farahbod Heidari1, Katia Zolotovsky2,3
1Department of Architecture, Tarbiat Modares University, Tehran, Iran.
3D printing and additive manufacturing
|May 1, 2025
概括
再生设计从可持续性转变为积极恢复生态和社会系统. 这项研究引入了一种数据驱动,自我适应的模型,用于建筑环境中的基于证据的环境管理.
科学领域:
- 环境科学 环境科学
- 系统设计 系统设计
- 生态工程生态工程生态工程
背景情况:
- 再生设计强调社会文化和生态系统之间的协同关系.
- 它需要从可持续模式转向促进循环系统和资源再生的整体范式.
- 当前的设计方法往往缺乏应对复杂环境挑战所需的综合方法.
研究的目的:
- 为再生自适应设计提出一个整体的,数据驱动的,自我适应的设计策略.
- 开发一个综合解决问题的模型,适用于各种规模,从微观到宏观.
- 通过设计为战略环境和自然资源管理提供以证据为基础的解决方案.
主要方法:
- 利用机器学习的反循环机制和嵌套的共同进化循环.
- 整合一个包容性的反循环框架与监控系统和直观的数据集.
- 在设计,施工和建筑环境的交叉点应用数据驱动,自我适应的解决问题的策略.
主要成果:
- 拟议的模型为再生自适应设计提供了一个全面的方法.
- 它使设计师,规划师和城市管理人员能够优化决策过程.
- 该框架支持精确的数据驱动反,用于管理各种规模的复杂系统.
结论:
- 再生设计提供了一个超越可持续性的范式转变,专注于恢复和平衡.
- 数据驱动,自我适应的策略对于有效的环境和自然资源管理至关重要.
- 这种综合模型提供了一个可扩展的解决方案,通过设计与环境和共存.
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