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Cited by 202BIM and Construction IntegrationArtificial Intelligence ApplicationsMachine Learning in Materials Science

GPT models in construction industry: Opportunities, limitations, and a use case validation

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Saka, Abdullahi, Taiwo, Ridwan, Saka, Nurudeen, Salami, Babatunde Abiodun, Ajayi, Saheed, Akande, Kabiru, Kazemi, Hadi·2024·Developments in the Built Environment
DOI 10.1016/j.dibe.2023.100300
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Abstract

Large Language Models (LLMs) trained on large data sets came into prominence in 2018 after Google introduced BERT. Subsequently, different LLMs such as GPT models from OpenAI have been released. These models perform well on diverse tasks and have been gaining widespread applications in fields such as business and education. However, little is known about the opportunities and challenges of using LLMs in the construction industry. Thus, this study aims to assess GPT models in the construction industry. A critical review, expert discussion and case study validation are employed to achieve the study's objectives. The findings revealed opportunities for GPT models throughout the project lifecycle. The challenges of leveraging GPT models are highlighted and a use case prototype is developed for materials selection and optimization. The findings of the study would be of benefit to researchers, practitioners and stakeholders, as it presents research vistas for LLMs in the construction industry.

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  • During the design phase, LLMs can perform code compliance verification, cost estimation, energy efficiency modeling, and automated design generation.

    “During the design phase, they perform code compliance verification, cost estimation, energy efficiency modeling, and automated design generation (Saka et al. 2023a, b ).”

    From A review of LLMs and their applications in the architecture, engineering and construction industry (2025) · cites this paper as Saka et al. 2023a, b

    Sections 5.2.1, 5.2.2, and 5.2.4 describe design-idea generation, automated code-compliance checks, and project costing as potential design-phase uses of GPT models. Section 5.2.5 discusses improving energy-efficiency analysis, including guidance on simulation tools and interpretation of results, but does not say the models independently perform energy-efficiency modeling. The paper presents these as opportunities rather than established capabilities across all four tasks. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • Saka et al. (2023a) present current applications leveraging models from the ChatGPT family, however only a limited number of them.

    “They present current applications leveraging models from the ChatGPT family, however only a limited num -ber of them.”

    From A review of LLMs and their applications in the architecture, engineering and construction industry (2025) · cites this paper as Saka et al. 2023a

  • Saka et al. (2023a) did a holistic review of GPT models in the construction industry, focusing on opportunities and limitations in all stages of construction.

    “Saka et al. ( 2023a ) did a holistic review of GPT models in the construction industry, focusing on opportunities and limitations in all stages of construction.”

    From A review of LLMs and their applications in the architecture, engineering and construction industry (2025) · cites this paper as Saka et al. 2023a

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Cited by

Sorted by citations

Authors
Saka, A., Taiwo, R., Saka, N., Salami, B. A., Ajayi, S., Akande, K., & Kazemi, H.
Affiliations
  • Leeds Beckett University (GB)
  • Hong Kong Polytechnic University (HK)
  • Cardiff Metropolitan University (GB)
Corresponding author
Ajayi, Saheed
ORCID
Venue
Developments in the Built Environment
Publisher
Elsevier BV
Published
2024-03
Volume
17
Pages
100300
ISSN
2666-1659 (ISSN-L)
DOI
10.1016/j.dibe.2023.100300
OpenAlex
W4389618734
Cited by
202
References
13
Citations by year
2026: 72 · 2025: 84 · 2024: 41 · 2023: 5
FWCI
24.26
Type
journal article
Language
en
Open access
Licence
Locations
  • Developments in the Built Environment (publishedVersion, cc-by)
  • Leeds Beckett Repository (Leeds Beckett University) (acceptedVersion)
  • WestminsterResearch (University of Westminster) (acceptedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • Figshare (submittedVersion, cc-by)
  • PolyU Institutional Research Archive (Hong Kong Polytechnic University) (submittedVersion, cc-by)
  • arXiv (Cornell University) (cc-by)
Funders
Leeds Beckett University
Topics
  • Physical Sciences › Engineering › Building and Construction › BIM and Construction Integration
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
  • Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
Keywords
GPT models, large language models, construction industry, construction project life cycle, material selection, material optimization

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