EDU-AI: a twofold machine learning model to support classroom layout generation

Creative Commons License

KARADAĞ İ., Guzelci O. Z., Alaçam S.

Construction Innovation, vol.23, no.4, pp.898-914, 2023 (ESCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 23 Issue: 4
  • Publication Date: 2023
  • Doi Number: 10.1108/ci-02-2022-0034
  • Journal Name: Construction Innovation
  • Journal Indexes: Emerging Sources Citation Index (ESCI), Scopus, ABI/INFORM, Aerospace Database, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, INSPEC, Metadex, Civil Engineering Abstracts
  • Page Numbers: pp.898-914
  • Keywords: Artificial intelligence, Machine learning, Generative adversarial networks, Architectural design, Classroom layout, Plan layout generation
  • Istanbul Technical University Affiliated: Yes


© 2022, Ilker Karadag, Orkan Zeynel Güzelci and Sema Alaçam.Purpose: This study aims to present a twofold machine learning (ML) model, namely, EDU-AI, and its implementation in educational buildings. The specific focus is on classroom layout design, which is investigated regarding implementation of ML in the early phases of design. Design/methodology/approach: This study introduces the framework of the EDU-AI, which adopts generative adversarial networks (GAN) architecture and Pix2Pix method. The processes of data collection, data set preparation, training, validation and evaluation for the proposed model are presented. The ML model is trained over two coupled data sets of classroom layouts extracted from a typical school project database of the Ministry of National Education of the Republic of Turkey and validated with foreign classroom boundaries. The generated classroom layouts are objectively evaluated through the structural similarity method (SSIM). Findings: The implementation of EDU-AI generates classroom layouts despite the use of a small data set. Objective evaluations show that EDU-AI can provide satisfactory outputs for given classroom boundaries regardless of shape complexity (reserved for validation and newly synthesized). Originality/value: EDU-AI specifically contributes to the automation of classroom layout generation using ML-based algorithms. EDU-AI’s two-step framework enables the generation of zoning for any given classroom boundary and furnishing for the previously generated zone. EDU-AI can also be used in the early design phase of school projects in other countries. It can be adapted to the architectural typologies involving footprint, zoning and furnishing relations.