Optimisation and Machine Learning for Climate-Friendly Design

2024

1. De Luca, F., Natanian, J., & Wortmann, T. (2024). Ten questions concerning environmental architectural design exploration. Building and Environment, 261, 111697. https://doi.org/10.1016/j.buildenv.2024.111697
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   BibTeX

   @article{DELUCA2024111697, abstract = {It is widely recognized that the sustainability of the built environment is one of the key strategies to tackle global crises such as climate change, resource depletion, and human health. An environmentally driven approach to architectural design and urban planning through simulations has been around for many decades. However, architects and planners haven't fully taken advantage of its potential yet due to the complexity of the tasks and the fragmentation of tools and methods in different fields of expertise. This paper aims to demonstrate how the nascent field of environmental architectural design exploration is a viable answer to complex tasks and challenges through the integration of sustainable design, environmental simulations, building performance analysis, and computational procedures leveraging parametric design environments. After defining the context that favored its development, the ten questions answered present the environmental metrics and design parameters, propose a taxonomy of simulation tools and methods, present optimization processes, identify the required data, and introduce a classification of co-simulation and design exploration workflows, emphasizing their potential for the design of sustainable buildings and districts. Then focus is given to the scales and domains of application and to the potential of machine learning methods. The paper concludes by presenting applications in practice and discussing challenges and future opportunities. Strategies and operational procedures emerge from the ten questions. They emphasize the use of holistic approaches and the selection of balanced design solutions allowed by environmental architectural design exploration for the development of a sustainable built environment.}, author = {{De Luca}, Francesco and Natanian, Jonathan and Wortmann, Thomas}, doi = {https://doi.org/10.1016/j.buildenv.2024.111697}, issn = {0360-1323}, journal = {Building and Environment}, language = {English}, pages = 111697, title = {Ten questions concerning environmental architectural design exploration}, url = {https://www.sciencedirect.com/science/article/pii/S0360132324005390}, volume = 261, year = 2024 }
2. Tay, J., Ortner, F. P., Wortmann, T., & Aydin, E. E. (2024). Computational Optimisation of Urban Design Models: A Systematic Literature Review. Urban Science, 8(3), Article 3. https://doi.org/10.3390/urbansci8030093
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   BibTeX

   @article{urbansci8030093, abstract = {The densification of urban spaces globally has contributed to a need for design tools supporting the planning of more sustainable, efficient, and liveable cities. Urban Design Optimisation (UDO) responds to this challenge by providing a means to explore many design solutions for a district, evaluate multiple objectives, and make informed selections from many Pareto-efficient solutions. UDO distinguishes itself from other forms of design optimisation by addressing the challenges of incorporating a wide range of planning goals, managing the complex interactions among various urban datasets, and considering the social–technical aspects of urban planning involving multiple stakeholders. Previous reviews focusing on specific topics within UDO do not sufficiently address these challenges. This PRISMA systematic literature review provides an overview of research on topics related to UDO from 2012 to 2022, with articles analysed across seven descriptive categories. This paper presents a discussion on the state-of-the-art and identified gaps present in each of the seven categories. Finally, this paper argues that additional research to improve the socio-technical understanding and usability of UDO would require: (i) methods of optimisation across multiple models, (ii) interfaces that address a multiplicity of stakeholders, (iii) exploration of frameworks for scenario building and backcasting, and (iv) advancing AI applications for UDO, including generalizable surrogates and user preference learning.}, article-number = {93}, author = {Tay, JingZhi and Ortner, Frederick Peter and Wortmann, Thomas and Aydin, Elif Esra}, doi = {10.3390/urbansci8030093}, issn = {2413-8851}, journal = {Urban Science}, number = 3, title = {Computational Optimisation of Urban Design Models: A Systematic Literature Review}, url = {https://www.mdpi.com/2413-8851/8/3/93}, volume = 8, year = 2024 }
3. Zhang, R., Xu, X., Liu, K., Kong, L., Wang, W., & Wortmann, T. (2024). Airflow modelling for building design: A designers’ review. Renewable and Sustainable Energy Reviews, 197, 114380. https://doi.org/10.1016/j.rser.2024.114380
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   BibTeX

   @article{ZHANG2024114380, abstract = {Sustainable design is no longer an emerging debate. Data-driven urban and architectural design faces several potential threats related to airflow modelling, including extreme wind environments, pollutant emissions, and the potential for new wind energy in design. Designers require advanced design tools to address these airflow modelling challenges. Still, it is difficult for designers to implement and utilise airflow models fully, and not all data-driven approaches are accessible to designers in traditional design workflow systems. This review systematically analyses the frontiers of airflow modelling in urban and architectural building design from the designer's perspective. It briefly summarises the advantages and disadvantages of airflow modelling based on the parametric design's white-box, black-box, and grey-box approaches. This review explores the challenges of wind environments and their analytical responses by screening design research articles at three scales. It summarises the eight main existing research directions. The article discusses the findings of designers at each scale, revealing significant areas of interest and continuing focus, such as the reduction of pollutant emissions and the interaction of new wind energy generation in design. This study concludes with three responses to possible challenges: improved resolution for design research loop, unification of information entropy in design and research, and airflow modelling tool-accessibility in the overall design ethos and education mindset; and provides illustrative vignettes of possible demos for the future development of airflow models' accessibility; and offers a vision of how designers better produce data-accessible design logic and fulfil their role in future sustainable design.}, author = {Zhang, Ran and Xu, Xiaodong and Liu, Ke and Kong, Lingyu and Wang, Wei and Wortmann, Thomas}, doi = {https://doi.org/10.1016/j.rser.2024.114380}, issn = {1364-0321}, journal = {Renewable and Sustainable Energy Reviews}, pages = 114380, title = {Airflow modelling for building design: A designers' review}, url = {https://www.sciencedirect.com/science/article/pii/S1364032124001035}, volume = 197, year = 2024 }