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Shape Grammar-Driven BIM Model Database for a Modular Building Design Recommender System

  • Saddiq Ur REHMAN (Department of Architecture, Graduate School of Kyung Hee University) ;
  • Inhan KIM (Department of Architecture, Graduate School of Kyung Hee University)
  • Published : 2024.07.29

Abstract

The absence of standardized architectural design configurations poses significant challenges in stakeholder collaboration during the pre-design phase, particularly for modular buildings. Clients often lack comprehensive knowledge of potential design configurations for their proposed buildings, leading to dissatisfaction upon project completion. This issue is exacerbated in office buildings where floor plan layouts are uncertain due to diverse employee needs. To address these challenges, this research introduces a BIM-based dataset for modular office buildings using shape grammar principles. The research manipulates the location of core modules (including staircases, elevator shafts, and entrances) based on shape grammar principles, resulting in twenty unique configurations that provide standardized options for BIM model development. The initial phase involves developing a naming convention based on shape grammar principles to determine the core module locations. Using this convention, BIM models for modular office buildings are created, forming a database connected to the frontend and backend of a recommender system. This system recommends three different design options to clients based on their specific needs, fostering improved collaboration in the pre-design phase by involving clients directly. The user interface of the recommender system aids clients in understanding potential office building configurations, thereby enhancing collaboration and decision-making. Through the amalgamation of shape grammar principles and BIM technology, this proposed system offers a promising approach to advancing efficiency and precision in architectural design communication and representation.

Keywords

Acknowledgement

This work is supported in 2024 by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant RS-2021-KA163269).

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