Deep-Performance - Incorporating Deep Learning for Automating Building Performance Simulation in Generative Systems

Yousif, Shermeen; Bolojan, Daniel

doi:10.52842/conf.caadria.2021.1.151

Cited by 11 publications

(10 citation statements)

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“…Performance based (Rahimian, 2022), (Han, 2022), (Xu, 2022), (Jia, 2021), (Singh & Geyer, 2022), (Paterson, et al, 2017), (Baghdadi, et al, 2020), (Wang, et al, 2019), (Li, et al, 2018), (He, et al, 2021), (Singaravel, et al, 2018), (Lin, et al, 2021), (Chokwitthaya, et al, 2019), (Gan, et al, 2019), (Olu-Ajayi, et al, 2022, (Zou, et al, 2021), (Li, et al, 2019), (Chou, Bui, 2014), (Wortmann, 2019), (Schwartz, et al, 2021), (Scherz, et al, 2022), (Sun, et al, 2015), (Mangan, 2021), (Ruiz, et al, 2017), (Singaravel, et al, 2018), (Toniolo, Leon, 2017), Multi objective optimization (Singaravel, et al, 2018), (Chardon, et al, 2016), (Natephra, et al, 2018), (Yousif & Bolojan, 2021), (Chardon, et al, 2015), (Liu, 2022), (Zhuang, et al, 2021), (Zhang, et al, 2021), (Baydoğan & Şener, 2014), (Chen & Pan, 2015), (Chen & Yang, 2017), (Si, et al, 2019), (Razmi, et al, 2022), (Carbonari, et al, 2019), (Kim & Clayton, 2020),…”

Section: Category Referencesmentioning

confidence: 99%

A systematic review on artificial intelligence applications in architecture

Bölek

Tutal

Özbaşaran

2023

DRArch

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Since the advent and usage of artificial intelligence approaches in architecture, a significant number of studies have focused on integrating technological solutions to architectural issues. Artificial intelligence applications in architectural design range from intelligent material design to architectural plan solutions. The ubiquity and distribution of research in this field, as well as the rising use of artificial intelligence techniques to solve design challenges, require an analytical classification of the essential literature review. This article presents a descriptive and analytical review of the work on artificial intelligence applications in architecture. A strong review has been made that identifies and addresses the gaps in artificial intelligence and architecture; and the literature review is transformed into statistical plots. The study's findings indicate a growing interest in artificial intelligence in the field of architecture. There is a need for novel research to be conducted in these areas using advanced technology and techniques.

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Section: Category Referencesmentioning

confidence: 99%

A systematic review on artificial intelligence applications in architecture

Bölek

Tutal

Özbaşaran

2023

DRArch

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“…The goal of objective AI, or more accurately, the several AI agents that form this module, is to process data based on the boundary conditions extracted from the subjective AI module and a given digital model detailed analysis of various regulations, simulations and analysis evaluations. The state-of-the-art research focused on creating "surrogate" models of underlying numerical simulations utilizing various neural network architectures [29,[58][59][60]. After training, such models can provide highly precise results in just a fraction of the standard computation time.…”

Section: Objective Aimentioning

confidence: 99%

Decision Support Systems in Architecture—A Future Perspective

Schubert,

Bratoev,

Petzold

2023

Buildings

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The benefits of design decision support systems (DDSSs) in the architectural planning context have been proven in research and are increasingly used in practice. The sense and purpose are apparent. The weighing of the most diverse ideas and approaches are required for design problems that cannot be solved unambiguously and are characterized by complex, open issues of architectural design tasks, coupled with contradictory criteria. DDSSs support planners/decision-makers with objective information to support the decision-making process with well-founded data and statements. This is becoming increasingly necessary, especially given increasingly complex construction tasks, and thus the difficult-to-predict effects of decisions. Taking this maxim into account, however, also reveals challenges in the planning context, as well as the immense potential and fields of application. Building on these issues, this article presents a perspective for DDSSs. The paper discusses the current focus and advancements of such systems, highlighting the challenges such tools still face, and provides a vision of the perspective future of these systems from reactive systems to proactive assistance.

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“…Despite acceptable visual outcomes, their output variety sometimes suffered, leading to similar results. Due to AI models pre-training on large datasets, not domain-specific ones (Yousif, Bolojan, 2021).…”

Section: Visual Inspiration and Creativitymentioning

confidence: 99%

Architectural alchemy: Leveraging Artificial Intelligence for inspired design – a comprehensive study of creativity, control, and collaboration

Petráková,

Šimkovič

2023

Architecture Papers of the Faculty of Architecture and Design STU

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The research paper contends that Artificial Intelligence (AI) serves as a collaborative partner in architectural design, rather than merely a utility tool. To substantiate this argument, a three-phase, nine-test investigation evaluating the strengths and limitations of two prominent AI platforms: Midjourney AI and Stable Diffusion was undertaken. These platforms synergize human creativity and AI capabilities through features like text prompts and image references, thereby fostering innovative avenues in architecture. Our analysis indicates that Midjourney AI is proficient in generating initial design concepts, largely thanks to its extensive data libraries, but is deficient in design refinement and user control. Conversely, Stable Diffusion empowers designers with greater control via features like ControlNet but sacrifices visual clarity due to its smaller generative models. Both platforms share a common flaw: an overemphasis on aesthetics and shape at the expense of functional understanding. Building upon these empirical observations, the paper outlines strategies for designers to reasonably leverage AI in optimising workflows. It confirms two key hypotheses concerning the interplay of creativity, control, and collaboration, emphasising that both human architects and AI systems benefit from iterative feedback and continuous adaptation. In summary, the study posits that AI is not just an adjunct technology but a transformative force with the capacity to fundamentally alter architectural design processes, paving the way for a new paradigm where human expertise and machine capabilities converge for enriched design outcomes.

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Deep-Performance - Incorporating Deep Learning for Automating Building Performance Simulation in Generative Systems

Cited by 11 publications

References 0 publications

A systematic review on artificial intelligence applications in architecture

A systematic review on artificial intelligence applications in architecture

Decision Support Systems in Architecture—A Future Perspective

Architectural alchemy: Leveraging Artificial Intelligence for inspired design – a comprehensive study of creativity, control, and collaboration

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