mlsanityData to 14 Oct 2026

GPT Models in Construction Industry: Opportunities, Limitations, and a Use Case Validation

arXiv 2305.18997 ↗First posted 30 May 2023cs.HC cs.AI cs.CLcited by 21

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Abstract (arXiv)

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 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.

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). Builds on 19 of its 153 references that are in the map. First posted on arXiv 30 May 2023.

Builds on

19 of 153 references.

References · 19

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2023GPT-4 Technical Reportcited by 18,396
  4. 2022Training language models to follow instructions with human feedbackcited by 15,971
  5. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  6. 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 4,057
  7. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,234
  8. 2022Survey of Hallucination in Natural Language Generationcited by 2,075
  9. 2023FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performancecited by 853
  10. 2023Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyondcited by 411
  11. 2022Text and Code Embeddings by Contrastive Pre-Trainingcited by 361
  12. 2022Efficient Training of Language Models to Fill in the Middlecited by 255
7 more
  1. 2023ChatGPT Beyond English: Towards a Comprehensive Evaluation of Large Language Models in Multilingual Learningcited by 206
  2. 2022A Survey of Controllable Text Generation using Transformer-based Pre-trained Language Modelscited by 129
  3. 2023An Overview on Language Models: Recent Developments and Outlookcited by 22
  4. 2023Investigating the use of ChatGPT for the scheduling of construction projectscited by 17
  5. 2023BIM-GPT: a Prompt-Based Virtual Assistant Framework for BIM Information Retrievalcited by 12
  6. 2023Robot-Enabled Construction Assembly with Automated Sequence Planning based on ChatGPT: RoboGPTcited by 11
  7. 2023Foundation Models for Natural Language Processing -- Pre-trained Language Models Integrating Mediacited by 8

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 9; 2025: 5; 2026: 5.

21 papers.

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  1. 2023From Concept to Manufacturing: Evaluating Vision-Language Models for Engineering Designcited by 51
  2. 2024Benchmarks as Microscopes: A Call for Model Metrologycited by 35
  3. 2025Large Language Models for Combinatorial Optimization: A Systematic Reviewcited by 20
  4. 2025Large Language Models for Computer-Aided Design: A Surveycited by 18
  5. 2025Using Vision Language Models for Safety Hazard Identification in Constructioncited by 13
  6. 2024GreedLlama: Performance of Financial Value-Aligned Large Language Models in Moral Reasoningcited by 9
  7. 2023Generative AI in the Construction Industry: Opportunities & Challengescited by 8
  8. 2024LLM-SEM: A Sentiment-Based Student Engagement Metric Using LLMS for E-Learning Platformscited by 6
  9. 2024CHATATC: Large Language Model-Driven Conversational Agents for Supporting Strategic Air Traffic Flow Managementcited by 5
  10. 2025Context-aware LLM-based AI Agents for Human-centered Energy Management Systems in Smart Buildingscited by 4
  11. 2024Overcoming LLM Challenges using RAG-Driven Precision in Coffee Leaf Disease Remediationcited by 3
  12. 2024Autonomous Building Cyber-Physical Systems Using Decentralized Autonomous Organizations, Digital Twins, and Large Language Modelcited by 3
9 more
  1. 2024Evaluating Large Language Models for Material Selectioncited by 2
  2. 2024AI's Spatial Intelligence: Evaluating AI's Understanding of Spatial Transformations in PSVT:R and Augmented Realitycited by 2
  3. 2025Perturbing the Derivative: Doubly Wild Refitting for Model-Free Evaluation of Opaque Machine Learning Predictorscited by 2
  4. 2026StructureClaw: Traceable LLM Agents and an Executable Benchmark for Structural Engineering Workflowscited by 2
  5. 2024Analyzing mixed construction and demolition waste in material recovery facilities: evolution, challenges, and applications of computer vision and deep learningcited by 1
  6. 2026Data-driven and distributed governance of building facilities management using decentralized autonomous organization, digital twin, and large language modelscited by 0
  7. 2026Training-Free Agentic Computer Vision for Structural Component Detection in 2D Structural Framing Planscited by 0
  8. 2026Agentic AI with Structured CoT for Enhancing AI's Spatial Intelligence: Visualization and Reasoning of Rotationcited by 0
  9. 2026Fine-Tuning VLM for Enhancing AI's Spatial Intelligence: Understanding 3D and 2D Rotationscited by 0