Architectural intelligence最新文献

Pressing problems in urban ventilation and thermal comfort affecting pedestrians related to current urban development and densification are increasingly dealt with from the perspective of climate change adaptation strategies. In recent research efforts, the prime objective is to accurately assess pedestrian-level wind (PLW) environments by using different simulation approaches that have reasonable computational time. This review aims to provide insights into the most recent PLW studies that use both established and data-driven simulation approaches during the last 5 years, covering 215 articles using computational fluid dynamics (CFD) and typical data-driven models. We observe that steady-state Reynolds-averaged Navier-Stokes (SRANS) simulations are still the most dominantly used approach. Due to the model uncertainty embedded in the SRANS approach, a sensitivity test is recommended as a remedial measure for using SRANS. Another noted thriving trend is conducting unsteady-state simulations using high-efficiency methods. Specifically, both the massively parallelized large-eddy simulation (LES) and hybrid LES-RANS offer high computational efficiency and accuracy. While data-driven models are in general believed to be more computationally efficient in predicting PLW dynamics, they in fact still call for substantial computational resources and efforts if the time for development, training and validation of a data-driven model is taken into account. The synthesized understanding of these modeling approaches is expected to facilitate the choosing of proper simulation approaches for PLW environment studies, to ultimately serving urban planning and building designs with respect to pedestrian comfort and urban ventilation assessment.

With the rise of the next generation of artificial intelligence driven by knowledge and data, the research on knowledge representation in architecture is also receiving widespread attention from the academia. This paper sorts out the evolution of architectural knowledge representation methods in the history of architecture, and summarizes three progressive representation frameworks of their development with type, pattern and network. By searching these three keywords in the Web of Science Core Collection among 4867 publications from 1990 to 2021, the number of publications in the past 5 years raised more than 50%, which show significant research interest in architecture industry in recent years. Among them, the first two are static declarative knowledge representation methods, while the network-based knowledge representation method also includes procedural knowledge representation methods and provides a way for knowledge association. This means the network representation has more advantage in terms of the logical completeness of knowledge representation, and accounts for 67% of the current research on knowledge representation in architecture. In the context of the rapid development of artificial intelligence, this method can realize the construction of architectural knowledge system and greatly improve the work efficiency of the building industry. On the other hand, in the face of carbon-neutral sustainable development scenarios, using knowledge representation, building performance knowledge and design knowledge could be expressed in a unified manner, and a personalized and efficient workflow for performance-oriented scheme design and optimization would be achieved.

Many in the design community have long claimed that digital mass-customization is cheaper, faster, smarter and more environmentally sustainable than the mechanical mass-production of standardized industrial products; and that the electronic transmission of information is cheaper, faster, smarter, and more environmentally sustainable than the mechanical transportation of people and goods. The global pandemic has tragically proven that a computational alternative to the modern, mechanical way of making, working, and living, now exists, and it is viable. When we had to shut down corporate offices, global megafactories, suburban shopping malls, and intercontinental airports, we did. We did because we had to; but also because today's technology already allows us to do so.

In recent years, topology optimization has become a popular strategy for creating elegant and innovative forms for architectural design. However, the use of existing topology optimization techniques in practical applications, especially for large-scale projects, is rare because the generated forms often cannot satisfy all the design requirements of architects and engineers. This paper identifies the limitations of commonly used assumptions in topology optimization and highlights the importance of having multiple solutions. We show how these limitations could be removed and present various techniques for generating diverse and competitive structural designs that are more useful for architects. Unlike conventional topology optimization, we may include load and support conditions as additional design variables to enhance the structural performance substantially. Furthermore, we show that varying the design domain provides a plethora of opportunities to achieve more-desirable design outcomes.