Architectural intelligence最新文献

While algorithms have become lifelike in their capability to generate original information with large data sets and processing power, they depart from life in their parallel production of misinformation and entropic acceleration. Cell evolution on earth over billions of years has to some extent prophesied the most effective evolutionary paths of digital technology: generating original information from normalization of large number of atoms and random variation through genetic mixing in prokaryotic and eukaryotic cells. Yet, cell evolution seems to operate according to the second law of thermodynamics in which entropy defines life processes. Algorithms have so far not responded to the subversion of life through misinformation and entropic acceleration. If we were to reverse misinformation and entropic acceleration, algorithms must return to life to merge the underlying principles of life on earth with the production of original information. Biology must take precedence over mathematics in our twenty-first century metaphysical formulations and materials production.

3D concrete printing (3DCP) technology is a construction method that offers a unique combination of automation and customization. However, when the printing area goes large, generating the print path becomes a sophisticated work. That’s because the customized print path should not only be expandable but also printable, such rules are hard to follow as both the printing area and construction requirements increase. In this paper, the Shenzhen Baoan 3D Printing Park project serves as a case study to introduce space-filling and print path generation methods for three types of large-area concrete pavement. The space-filling methods utilize geometry-based rules to generate complex and expandable paving patterns, while the print path generation methods utilize construction-oriented rules to convert these patterns into print paths. The research provides easy-to-operate design and programming workflows to achieve a pavement printing area of 836 sqm, which significantly increases the construction scale of large-format additive manufacturing (LFAM) and shows the potential of 3D printing technology to reach non-standard results by using standard workflows.

This paper aims to identify the current status of research in 3D concrete printing (3DCP), locate the sustainability considerations relevant to these trajectories, and to identify a gap in knowledge and future research challenge regarding the sustainability of 3DCP. To categorize the broad range of research topics within 3DCP, the paper introduces an assessment framework that subdivides this field into three sub-fields: material science, computational design, and structure and performance. Common sustainability considerations are identified for each of these sub-fields. As a result of this analysis, a lack of critical assessments on claims about the sustainability and environmental impacts of 3DCP is identified.

Our survey of literature, and its analysis via this framework, finds that whilst certain sustainability aspects are highlighted, other measures and considerations are skimmed over, or omitted. It is found that whilst material optimization and the ability to create formwork-free, complex forms is noted as a main argument for the implementation of 3DCP, this claim is largely unsupported by reference or reported outcomes, and the environmental impacts are often only briefly discussed. There is a clear need for a holistic view on the sustainability issues which surround 3DCP.

This paper further highlights the lack of comprehensive assessment tools and metrics for measuring the environmental impact of 3DCP and concludes that further research must be done to develop these tools, to allow architects to integrate 3DCP into sustainability-oriented design workflows. Our paper concludes that the development of these tools will lead to a more comprehensive understanding on the environmental sustainability of 3DCP, allowing research resources to be focused within each field to ensure 3DCP continues to develop in a sustainable way.

Tou-Kung, which is pronounced in Chinese and known as Bracket Set (Liang & Fairbank, A pictorial history of Chinese architecture, 1984), is a vital support component in the Chinese traditional wooden tectonic systems. It is located between the column and the beam and connects the eave and pillar, making the heavy roof extend out of the eaves longer. The development of Tou-Kung is entirely a microcosm of the development of ancient Chinese architecture; the aesthetic structure and Asian artistic temperament behind Tou-Kung make it gradually become the cultural and spiritual symbol of traditional Chinese architecture. In the contemporary era, inheriting and developing Tou-Kung has become an essential issue. Several architects have attempted to employ new materials and techniques to integrate the traditional Tou-Kung into modern architectural systems, such as the China Pavilion at the 2010 World Expo and Yusuhara Wooden Bridge Museum. This paper introduces the topological optimisation method bi-directional evolutionary structural optimisation (BESO) for form-finding. BESO method is one of the most popular topology optimisation methods widely employed in civil engineering and architecture. Through analyzing the development trend of Tou-Kung and mechanical structure, the authors integrate 2D and 3D optimisation methods and apply the hybrid methods to form-finding. Meanwhile, mortise and tenon joint used to create stable connections with components of Tou-Kung are retained. This research aims to design a new Tou-Kung corresponding to “structural performance-based aesthetics”. The workflow proposed in this paper is valuable for Architrave and other traditional building components.

The paper changes the focus of the design debate from a human-centered design methodology to a posthuman design that takes both human and nonhuman agents into account. It examines how designers might use a multispecies perspective to produce projects with distinguished intelligence and performance. To illustrate this, we describe a project of structures for plants that started on a course in an academic setting. The project methodology begins with “Thing Ethnography”, investigating the movement of a water bottle inside a house and its interaction with other objects. The correlation between water and plants was decided to be further expanded, considering how water might enhance the environmental humidity and create a cooler microclimate for indoor plants. According to their effectiveness, 3D-printed biomimetic structures were designed and manufactured as water droplet supports considering different materials, and positioned in various configurations around a plant. Humidity levels and temperature of the structures were measured. As a result, this created a novel method for mass customization and working with plants. The paper discusses the resultant evidence-based design and the environmental values related to it.

As the tech world moves increasingly toward an AI-generated virtual universe — the so-called “metaverse” — new paradigms define the impacts of this technology on its human users. AI and VR, like the Internet before them, offer both remarkable opportunities and pitfalls. Virtual Reality constitutes a new kind of human environment, and experiencing it relies upon human neurological mechanisms evolved to negotiate — and survive in — our ancestral physical environments. Despite the unrestricted freedom of designing the virtual universe, interacting with it is affected strongly by the body’s built-in physiological and psychological constraints. The eventual success of the metaverse will be determined by how successfully its designers manage to accommodate unconscious mechanisms of emotional attachment and wellbeing. Some fundamental misunderstandings coming from antiquated design models have influenced virtual environmental structures. It is likely that those design decisions may be handicapping the metaverse’s ultimate appeal and utility.

Situated in the field of architectural biodesign, InterspeciesForms explores a closer relationship between the fungus Pleurotus ostreatus and the designer in the creation of form. The intention of hybridizing mycelia’s agency of growth with architectural design aesthetic, is to generate novel, non- indexical crossbred designed outcomes. The purpose of this research to advance architecture's existing relationship with the biological and evolve preconceived notions of form. In order to establish a direct dialogue between architectural and mycelia agencies, robotic feedback systems are implemented to extract data from the physical realm and feed it into the digital. Initiating this cyclic feedback system, mycelia growth is scanned in order to computationally visualize its entangled network and agency of growth. Utilizing mycelia’s physical data as impute, the architect then embeds design intention into this process through customized algorithms based on the logic of stigmergy. In order to bring this cross-bred computational outcome back into the physical realm, form is 3D printed with a customized mixture of mycelium and agricultural waste. Once the geometry has been extruded, the robot patiently waits for the mycelia to grow and react to the organic 3D- printed compound. The architect then responds with a countermove, by scanning this new growth and continuing the cyclic feedback system between nature-machine and the architect. This procedure demonstrates form emerging in real time according to the co-creational design process and dynamic dialogue between architectural and mycelia agencies.

Under the influence of globalization, the transformation of traditional architectural space is vital to the growth of local architecture. As an important spatial element of traditional gardens, Taihu stone has the image qualities of being “thin, wrinkled, leaky and transparent” The “transparency” and “ leaky” of Taihu stone reflect the connectivity and irregularity of Taihu stone’s holes, which are consistent with the contemporary architectural design concepts of fluid space and transparency. Nonetheless, relatively few theoretical studies have been conducted on the spatial analysis and design transformation of Taihu stone. Using machine learning, we attempt to extract the three-dimensional spatial variation pattern of Taihu stone in this paper. This study extracts 3D spatial features for experiments using artificial neural networks (ANN) and generative adversarial networks (GAN). In order to extract 3D spatial variation patterns, the machine learning model learns the variation patterns between adjacent sections. The trained machine learning model is capable of generating a series of spatial sections with the spatial variation pattern of the Taihu stone. The purpose of the experimental results is to compare the performance of various machine learning models for 3D space learning in order to identify a model with superior performance. This paper also presents a novel concept for machine learning to master continuous 3D spatial features.