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Architectural Adaptation as Praxis 建筑适应性实践
Q4 Engineering Pub Date : 2022-07-01 DOI: 10.47982/spool.2022.2.03
Marie Ulber, Mona Mahall, A. Serbest
Since industrialization, modern architecture has appropriated the notion of adaptation. Defined as the adjustment of a building to the environment and its users, architectural adaptation has been mainly carried out via a narrow technological approach. Thus, digitalization has emerged as the latest ‘smart’ update. The limits of technological adaptation become especially evident with architecture in aiming to solve an ecological and social crisis on both a global and local level. In this paper, we argue for reconceptualizing adaptivity in architecture to (re)integrate processual, social, and aesthetic dimensions. We propose a new architectural understanding of adaptivity that includes currently excluded agents and involves them in communication and adaptation processes. As we focus on the intertwining of technical developments and cultural practices, that is, the interactions of human and non-human agents in architecture, we seek to describe architectural adaptation as an inclusive spatial praxis. This may aid in inventing new ways of life built upon sustainable nature-culture-technology relationships within society.
自工业化以来,现代建筑采用了适应的概念。建筑适应性被定义为建筑对环境和使用者的调整,主要是通过一种狭窄的技术方法来实现的。因此,数字化已成为最新的“智能”更新。在旨在解决全球和地方层面的生态和社会危机的建筑中,技术适应的局限性变得尤为明显。在本文中,我们主张重新定义建筑的适应性,以(重新)整合过程、社会和美学维度。我们提出了一种新的适应性架构理解,包括目前被排除在外的代理,并将它们纳入通信和适应过程。当我们关注技术发展和文化实践的交织时,即建筑中人类和非人类因素的相互作用,我们试图将建筑适应描述为一种包容性的空间实践。这可能有助于在社会中建立可持续的自然-文化-技术关系的基础上发明新的生活方式。
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引用次数: 0
‘Greening’ the Cities 绿化城市
Q4 Engineering Pub Date : 2022-05-27 DOI: 10.47982/spool.2022.1.01
Sinéad Nicholson, Marika Tomasi, Daniele Belleri, C. Ratti, M. Nikolopoulou
We are facing an urgent global environmental crisis that requires a reframing of traditional professional and conceptual boundaries within the urban environment. Complex and multidisciplinary issues need complex and multidisciplinary solutions, which result from the collaboration of many different disciplines concerned with the urban environment. A more integrated ecological perspective that recognizes the complexity of urban environments and resituates our ‘artificial’ or human-made world within its natural ecosystem can facilitate this shift towards greater knowledge exchange. C40 Cities case studies provide a framework within which to understand the disciplines and scales encompassed by ecological solutions, while projects at MIT Senseable City Lab and CRA-Carlo Ratti Associati highlight how data is used as a tool in driving ecological solutions.  The artificial world of sensors, data and networks creates a bridge between the ‘artificial’ and ‘natural’ elements of our urban environments, allowing us to fully understand the present condition, connect city users and decision makers, and better integrate ecological solutions into the built environment.
我们正面临着一场紧迫的全球环境危机,需要在城市环境中重新构建传统的专业和概念界限。复杂和多学科的问题需要复杂和多学科的解决方案,这是与城市环境有关的许多不同学科合作的结果。认识到城市环境的复杂性,并在自然生态系统中恢复我们的“人工”或人造世界的更综合的生态观点,可以促进这种向更大的知识交流的转变。C40城市案例研究为理解生态解决方案所包含的学科和规模提供了一个框架,而麻省理工学院可感知城市实验室和CRA-Carlo Ratti Associati的项目则强调了如何将数据用作推动生态解决方案的工具。由传感器、数据和网络组成的人工世界在城市环境的“人工”和“自然”元素之间架起了一座桥梁,使我们能够充分了解现状,连接城市用户和决策者,更好地将生态解决方案融入建筑环境。
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引用次数: 1
Data-driven design for Architecture and Environment Integration 面向体系结构与环境集成的数据驱动设计
Q4 Engineering Pub Date : 2022-05-27 DOI: 10.47982/spool.2022.1.02
D. Sunguroğlu Hensel, Jakub Tyc, M. Hensel
Rapid urbanization and related land cover and land use changes are primary causes of climate change, and of environmental and ecosystem degradation. Sustainability problems are becoming increasingly complex due to these developments. At the same time vast amounts of data on urbanization, construction and resulting environmental conditions are being generated. Yet it is hardly possible to gain insights for sustainable plan-ning and design at the same rate as data is generated. Moreover, the complexity of compound sustainability problems requires interdisciplinary approaches that address multiple knowledge fields, multiple dynamics and multiple spatial, temporal and functional scales. This raises a question regarding methods and tools available to planners and architects for tackling these complex issues. To address this problem we are developing an interdisciplinary approach, computational framework and related workflows for multi-domain and trans-scalar modelling that integrate planning and design scales. For this article two lines of research were selected. The first focuses on understanding environments for the purpose of discovering, recovering and adapting land knowledge to different conditions and contexts. This entails an analytical data-integrated computational workflow. The second line of research focuses on designing environments and developing an approach and computational workflow for data-integrated planning and design. These two lines converge in a combined analytical and generative data-integrated computational workflow. This combined approach aims for an intense integration of architectures and environments that we call embedded architectures. In this article we discuss the two lines of research, their convergence, and further research questions.
快速的城市化以及相关的土地覆盖和土地利用变化是气候变化以及环境和生态系统退化的主要原因。由于这些发展,可持续性问题变得越来越复杂。与此同时,正在生成大量关于城市化、建筑和由此产生的环境条件的数据。然而,很难以产生数据的速度获得可持续规划和设计的见解。此外,复合可持续性问题的复杂性需要跨学科的方法来解决多个知识领域、多个动态以及多个空间、时间和功能尺度。这就提出了一个关于规划者和架构师可用于解决这些复杂问题的方法和工具的问题。为了解决这个问题,我们正在开发一种跨学科的方法、计算框架和相关工作流程,用于集成规划和设计规模的多领域和跨标量建模。本文选择了两条研究路线。第一个重点是了解环境,以便发现、恢复土地知识并使其适应不同的条件和背景。这需要一个分析数据集成的计算工作流程。第二条研究重点是设计环境,开发数据集成规划和设计的方法和计算工作流。这两条线汇聚在一个分析和生成数据相结合的计算工作流中。这种组合方法旨在实现架构和环境的高度集成,我们称之为嵌入式架构。在这篇文章中,我们讨论了这两条研究路线,它们的趋同,以及进一步的研究问题。
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引用次数: 0
Bio-Cyber-Physical ‘Planetoids’ for Repopulating Residual Spaces 用于重新填充剩余空间的生物信息物理“小行星”
Q4 Engineering Pub Date : 2022-05-27 DOI: 10.47982/spool.2022.1.04
P. Oskam, H. Bier, Hamed Alavi
Minimal interventions that provide various microclimates can stimulate both biodiversity and social accessibility of leftover spaces. New habitats are often developed for different animal and plant species based on studies of the microclimates typical of such residual spaces. By introducing interventions of 0.5-1.0 m diameter ‘planetoids’ placed at various locations, existing and new life is supported. The ‘planetoid’ described in this paper is prototyped by means of Design-to-Robotic-Production and -Operation (D2RP&O). This implies that it is not only produced by robotic means, but that it contains sensor-actuator mechanisms that allow humans to interact with them by establishing a bio-cyber-physical feedback loop.
提供各种小气候的最小干预可以刺激生物多样性和剩余空间的社会可达性。根据对这些剩余空间的典型小气候的研究,通常会为不同的动植物物种开发新的栖息地。通过在不同位置引入直径0.5-1.0米的“小行星”,支持现有和新的生命。本文描述的“小行星”是通过设计到机器人生产和操作(D2RP&O)的方式进行原型设计的。这意味着它不仅是由机器人生产的,而且它包含传感器-执行器机制,允许人类通过建立生物网络-物理反馈回路与它们互动。
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引用次数: 0
Data-driven Urban Design 数据驱动的城市设计
Q4 Engineering Pub Date : 2022-05-27 DOI: 10.47982/spool.2022.1.03
Jeroen van Ameijde
Nicholas Negroponte and MIT’s Architecture Machine Group speculated in the 1970s about computational processes that were open to participation, incorporating end-user preferences and democratizing urban design. Today’s ‘smart city’ technologies, using the monitoring of people’s movement and activity patterns to offer more effective and responsive services, might seem like contemporary interpretations of Negroponte’s vision, yet many of the collectors of user information are disconnected from urban policy making. This article presents a series of theoretical and procedural experiments conducted through academic research and teaching, developing user-driven generative design processes in the spirit of ‘The Architecture Machine’. It explores how new computational tools for site analysis and monitoring can enable data-driven urban place studies, and how these can be connected to generative strategies for public spaces and environments at various scales. By breaking down these processes into separate components of gathering, analysing, translating and implementing data, and conceptualizing them in relation to urban theory, it is shown how data-driven urban design processes can be conceived as an open-ended toolkit to achieve various types of user-driven outcomes. It is argued that architects and urban designers are uniquely situated to reflect on the benefits and value systems that control data-driven processes, and should deploy these to deliver more resilient, liveable and participatory urban spaces.
Nicholas Negroponte和麻省理工学院的建筑机器小组在20世纪70年代推测了开放参与的计算过程,结合了最终用户的偏好,并使城市设计民主化。今天的“智能城市”技术,利用对人们行动和活动模式的监测,提供更有效、更快速的服务,似乎是对内格罗蓬特愿景的当代诠释,但许多用户信息的收集者与城市政策制定脱节。本文通过学术研究和教学进行了一系列理论和程序实验,以“建筑机器”的精神开发了用户驱动的生成设计过程。它探讨了用于场地分析和监测的新计算工具如何能够实现数据驱动的城市场所研究,以及这些工具如何与各种规模的公共空间和环境的生成策略相联系。通过将这些过程分解为收集、分析、翻译和实施数据的单独组成部分,并将其与城市理论相结合,展示了如何将数据驱动的城市设计过程视为一个开放式工具包,以实现各种类型的用户驱动结果。有人认为,建筑师和城市设计师处于独特的位置,能够反思控制数据驱动过程的利益和价值体系,并应部署这些系统,以提供更具弹性、宜居和参与性的城市空间。
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引用次数: 0
Interdisciplinary Data-integrated Approaches 跨学科数据集成方法
Q4 Engineering Pub Date : 2022-05-27 DOI: 10.47982/spool.2022.1.00
M. Hensel, H. Bier
Rapid urbanization with the associated land cover and land use change, as well as resource depletion, contribute to the degradation of ecosystems and biodiversity and have a negative impact on human health and well-being. Societal calls for responses and results pose a significant challenge for research and education in the various fields concerned with the environment. Alongside the current environmental crisis there is a pressing need for developing ‘green solutions’ for the built environment with the help of data-driven methods, workflows and tools. In view these developments, a shift from narrow disciplinary and domain-specific approaches towards broader interdisciplinary, multi-domain and multi-scalar strategies is required. This includes data-acquisition, data-sharing and data-integration, as well as data-driven modelling to enable the complexity of sustainability problems arising from rapid urbanization to be tackled. While there have been efforts to address the challenges of multi-domain approaches, for instance in the fields of sustainability, the urban and architectural sciences, as well as the interoperability of methods and tools, the actual problem goes deeper, requiring interdisciplinary knowledge exchange to develop adequate shared paradigms, concepts, methods and tools.  Cyber-physical Architecture (CpA) issue 5 addresses these challenges by engaging with experts from a range of disciplines involved in environmental concerns while utilizing data-acquisition, data-sharing and integration, and data-driven modelling in a discourse that identifies modalities for a broader interdisciplinary, multi-domain and multi-scalar approach.
快速城市化伴随着相关的土地覆盖和土地利用变化以及资源枯竭,助长了生态系统和生物多样性的退化,并对人类健康和福祉产生负面影响。社会对反应和结果的要求对与环境有关的各个领域的研究和教育构成了重大挑战。除了当前的环境危机,迫切需要在数据驱动的方法、工作流程和工具的帮助下,为建筑环境开发“绿色解决方案”。鉴于这些发展,需要从狭窄的学科和特定领域的方法转向更广泛的跨学科、多领域和多标量战略。这包括数据获取、数据共享和数据整合,以及数据驱动的建模,以便能够解决快速城市化所产生的可持续性问题的复杂性。虽然已经努力解决多领域方法的挑战,例如在可持续性、城市和建筑科学领域,以及方法和工具的互操作性,但实际问题更加深刻,需要跨学科的知识交流,以发展充分的共享范例、概念、方法和工具。网络物理架构(CpA)第5期通过与涉及环境问题的一系列学科的专家合作,同时利用数据采集、数据共享和集成以及数据驱动建模,在一篇论述中确定更广泛的跨学科、多领域和多标量方法的模式,解决了这些挑战。
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引用次数: 0
Indoor Air Quality Forecast in Shared Spaces– Predictive Models and Adaptive Design Proposals 共享空间的室内空气质量预测-预测模型和适应性设计建议
Q4 Engineering Pub Date : 2022-05-27 DOI: 10.47982/spool.2022.1.05
Hamed S. Alavi, Sailin Zhong, D. Lalanne
The high concentration of air pollutants in indoor environments can have a remarkable adverse impact on health and well-being, cognitive performance and productivity. Indoor air pollutants are especially problematic in naturally ventilated shared spaces such as classrooms and meeting rooms, where human-generated pollutants can rise rapidly. When the inhabitants are exposed to indoor air pollution, recovering from its ramifications takes time and harms their well-being in the long run. In our approach, we seek to predict and prevent such hazardous situations instead of rectifying them after they happen. The prediction and prevention are accomplished through algorithms that can learn from the evolution of air pollutants and other variables to indicate whether or not a high level of pollution is forecast. We present two AI-enabled methods, one providing the forecast for the concentration level of carbon dioxide in the next 5 and 20 minutes with 86% and 92% accuracy. The second algorithm provides predictive indicators about how the CO2 level will evolve during the upcoming session (meeting or a course) before the session starts. We will discuss design implications and present design proposals on how these methods can inform interactive solutions for preventing high concentrations of indoor air pollutants.
室内环境中高浓度的空气污染物会对健康和福祉、认知能力和生产力产生显著的不利影响。在教室和会议室等自然通风的共享空间,室内空气污染物的问题尤其严重,在这些空间,人为产生的污染物可能会迅速上升。当居民暴露于室内空气污染时,从其后果中恢复需要时间,从长远来看会损害他们的健康。在我们的方法中,我们寻求预测和预防这种危险情况,而不是在它们发生后加以纠正。预测和预防是通过算法完成的,该算法可以从空气污染物的演变和其他变量中学习,以指示是否预测高污染水平。我们提出了两种支持人工智能的方法,其中一种方法提供了未来5分钟和20分钟内二氧化碳浓度水平的预测,准确率分别为86%和92%。第二种算法在即将到来的会议(会议或课程)开始之前提供关于二氧化碳水平如何演变的预测指标。我们将讨论设计含义,并提出设计建议,说明这些方法如何为防止高浓度室内空气污染物的交互解决方案提供信息。
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