Recent Research Activities (Selected):
Deep-Performance: Incorporating Deep Learning for Automating Building Performance Simulation in Generative Systems.
Paper presented at the CAADRIA 2021 conference. Authors: Shermeen Yousif and Daniel Bolojan.
In this study, we introduce a newly developed method called Deep-Performance, to enable automatic environmental performance simulation prediction without the need to perform simulations, by integrating deep learning strategies. The aim is to train neural networks on datasets with thousands of building design samples and their corresponding performance simulation. The trained model would offer performance prediction for design options emerging in generative protocols. The research is a work-in-progress within a broader project aimed at automating buildings’ environmental performance evaluations of daylight analysis and energy simulation, using deep learning (DL) models. This paper focuses on the implementation of a supervised DL method for automating the retrieval of daylight analysis metrics, targeting successful daylight design and higher building enclosure efficiency. We have further improved a Pix2Pix model trained on 5 different datasets, each containing 6000 paired images of architectural floor plans and their daylight simulation metrics. In the inference phase, the model was able to accurately predict the daylight simulation for unseen sets of floor plans. For validation, two quantitative assessment metrics were followed to assess the predicted daylight performance against the daylight performance simulation. Both assessment metrics showed high accuracy levels.
Creative AI Ecologies
A research project and a workshop led by: Daniel Bolojan, Shermeen Yousif, Emmanouil Vermisso.
In light of the observed integration of Artificial Intelligence within many industries, this workshop reconsiders the “Architectural Design Cycle”, proposing nested generative AI design processes. Rather than thinking about AI as a “closed” cycle of “input-output”, a series of complementary deep neural networks examine the potential of a logical continuity in AI-driven workflows for architecture, simultaneously challenging and augmenting the designer’s agency. The workshop will deploy AI creativity to tackle a variety of architectural systems, including formal articulation, structural logic, and enclosure responsiveness. Combining parametric and AI layers by means of “domain-transfers” and “representation learning”, two parallel iterative workflows will address design at different scales (Large, Small).
The Role of AI in Architecture Design
A presentation for Research in Action series, Division of Research, Florida Atlantic University.
The session discusses the research and the role of artificial intelligence (AI) in the architecture design process. It covers topics of: the current role of AI, deep learning strategies for an enhanced computational design model, and potentials and challenges of AI creativity for building performance.