Generative BIM workspace for AEC conceptual design automation: prototype development

Sepehr Abrishami*, Jack Goulding, Farzad Pour Rahimian

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Purpose: The integration and automation of the whole design and implementation process has become a pivotal factor in construction projects. Problems of process integration, particularly at the conceptual design stage often manifest through a number of significant areas, from: design representation, cognition and translation, through to process fragmentation and loss of design integrity. Whilst Building Information Modelling (BIM) applications can be used to support design automation, particularly through the modelling, amendment, and management stages, they do not explicitly provide whole design integration. This is a significant challenge. However, advances in generative design now offers significant potential for enhancing the design experience to mitigate this challenge.

Design/Methodology/Approach: The approach outlined in this paper specifically addresses BIM deficiencies at the conceptual design stage. Where, the core drivers and indicators of BIM and generative design are identified and mapped into a G-BIM framework, and subsequently embedded into a Generative BIM (G-BIM) prototype. This actively engages generative design methods into a single dynamic BIM environment to support the early conceptual design process. The developed prototype followed the CIFE “horseshoe” methodology of aligning theoretical research with scientific methods to procure Architecture, Construction and Engineering (AEC) – based solutions. This G-BIM prototype was also tested and validated through a focus group workshop engaging five AEC domain experts.

Findings: The G-BIM prototype presents a valuable set of rubrics to support the conceptual design stage using generative design. It benefits from the advanced features of BIM tools in relation to illustration and collaboration (coupled with BIM’s parametric change management features).

Research Limitations/Implications: This prototype has been evaluated through multiple projects and scenarios. However, additional test data is needed to further improve system veracity using conventional and non-standard real-life design settings (and contexts). This will be reported in later works.

Originality/Value: Originality and value rests with addressing the shortcomings of previous research on automation during the design process. It also addresses novel computational issues relating to the implementation of generative design systems; where for example, instead of engaging static and formal description of the domain concepts, G-BIM actively enhances the applicability of BIM during the early design stages to generate optimised (and more purposeful) design solutions.
Original languageEnglish
Number of pages28
JournalEngineering, Construction and Architectural Management
Early online date10 Jul 2020
Publication statusEarly online - 10 Jul 2020


  • Generative Design
  • Genetic Algorithm
  • Space Syntax
  • AI
  • Conceptual Design


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