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

arXiv: https://arxiv.org/abs/2305.18997 · HTML: https://mlsanity.com/paper/2305.18997/

## 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 (most cited first, 19 of 19)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance](https://mlsanity.com/paper/2305.05176/) (2023)
- [Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond](https://mlsanity.com/paper/2304.13712/) (2023)
- [Text and Code Embeddings by Contrastive Pre-Training](https://mlsanity.com/paper/2201.10005/) (2022)
- [Efficient Training of Language Models to Fill in the Middle](https://mlsanity.com/paper/2207.14255/) (2022)
- [ChatGPT Beyond English: Towards a Comprehensive Evaluation of Large Language Models in Multilingual Learning](https://mlsanity.com/paper/2304.05613/) (2023)
- [A Survey of Controllable Text Generation using Transformer-based Pre-trained Language Models](https://mlsanity.com/paper/2201.05337/) (2022)
- [An Overview on Language Models: Recent Developments and Outlook](https://mlsanity.com/paper/2303.05759/) (2023)
- [Investigating the use of ChatGPT for the scheduling of construction projects](https://mlsanity.com/paper/2302.02805/) (2023)
- [BIM-GPT: a Prompt-Based Virtual Assistant Framework for BIM Information Retrieval](https://mlsanity.com/paper/2304.09333/) (2023)
- [Robot-Enabled Construction Assembly with Automated Sequence Planning based on ChatGPT: RoboGPT](https://mlsanity.com/paper/2304.11018/) (2023)
- [Foundation Models for Natural Language Processing -- Pre-trained Language Models Integrating Media](https://mlsanity.com/paper/2302.08575/) (2023)

## Built on by (most cited first, 21 of 21)

- [From Concept to Manufacturing: Evaluating Vision-Language Models for Engineering Design](https://mlsanity.com/paper/2311.12668/) (2023)
- [Benchmarks as Microscopes: A Call for Model Metrology](https://mlsanity.com/paper/2407.16711/) (2024)
- [Large Language Models for Combinatorial Optimization: A Systematic Review](https://mlsanity.com/paper/2507.03637/) (2025)
- [Large Language Models for Computer-Aided Design: A Survey](https://mlsanity.com/paper/2505.08137/) (2025)
- [Using Vision Language Models for Safety Hazard Identification in Construction](https://mlsanity.com/paper/2504.09083/) (2025)
- [GreedLlama: Performance of Financial Value-Aligned Large Language Models in Moral Reasoning](https://mlsanity.com/paper/2404.02934/) (2024)
- [Generative AI in the Construction Industry: Opportunities & Challenges](https://mlsanity.com/paper/2310.04427/) (2023)
- [LLM-SEM: A Sentiment-Based Student Engagement Metric Using LLMS for E-Learning Platforms](https://mlsanity.com/paper/2412.13765/) (2024)
- [CHATATC: Large Language Model-Driven Conversational Agents for Supporting Strategic Air Traffic Flow Management](https://mlsanity.com/paper/2402.14850/) (2024)
- [Context-aware LLM-based AI Agents for Human-centered Energy Management Systems in Smart Buildings](https://mlsanity.com/paper/2512.25055/) (2025)
- [Overcoming LLM Challenges using RAG-Driven Precision in Coffee Leaf Disease Remediation](https://mlsanity.com/paper/2405.01310/) (2024)
- [Autonomous Building Cyber-Physical Systems Using Decentralized Autonomous Organizations, Digital Twins, and Large Language Model](https://mlsanity.com/paper/2410.19262/) (2024)
- [Evaluating Large Language Models for Material Selection](https://mlsanity.com/paper/2405.03695/) (2024)
- [AI's Spatial Intelligence: Evaluating AI's Understanding of Spatial Transformations in PSVT:R and Augmented Reality](https://mlsanity.com/paper/2411.06269/) (2024)
- [Perturbing the Derivative: Doubly Wild Refitting for Model-Free Evaluation of Opaque Machine Learning Predictors](https://mlsanity.com/paper/2511.18789/) (2025)
- [StructureClaw: Traceable LLM Agents and an Executable Benchmark for Structural Engineering Workflows](https://mlsanity.com/paper/2607.14896/) (2026)
- [Analyzing mixed construction and demolition waste in material recovery facilities: evolution, challenges, and applications of computer vision and deep learning](https://mlsanity.com/paper/2409.13112/) (2024)
- [Data-driven and distributed governance of building facilities management using decentralized autonomous organization, digital twin, and large language models](https://mlsanity.com/paper/2605.16298/) (2026)
- [Training-Free Agentic Computer Vision for Structural Component Detection in 2D Structural Framing Plans](https://mlsanity.com/paper/2608.17237/) (2026)
- [Agentic AI with Structured CoT for Enhancing AI's Spatial Intelligence: Visualization and Reasoning of Rotation](https://mlsanity.com/paper/2610.04188/) (2026)
- [Fine-Tuning VLM for Enhancing AI's Spatial Intelligence: Understanding 3D and 2D Rotations](https://mlsanity.com/paper/2610.04206/) (2026)

## 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.