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

被引:45
作者
Saka, Abdullahi [1 ]
Taiwo, Ridwan [2 ]
Saka, Nurudeen [1 ]
Salami, Babatunde Abiodun [3 ]
Ajayi, Saheed [1 ]
Akande, Kabiru [4 ]
Kazemi, Hadi [1 ]
机构
[1] Leeds Beckett Univ, Sch Built Environm Engn & Comp, Leeds, England
[2] Hong Kong Polytech Univ, Dept Bldg & Real Estate, Hong Kong, Peoples R China
[3] Cardiff Metropolitan Univ, Cardiff Sch Management, Llandaff Campus, Cardiff, Wales
[4] OVO Energy, London, England
来源
DEVELOPMENTS IN THE BUILT ENVIRONMENT | 2024年 / 17卷
关键词
LLMs; ChatGPT; GPT; Artificial intelligence; Generative AI; ARTIFICIAL-INTELLIGENCE; BIG DATA; MANAGEMENT; BUILDINGS; PROJECTS; OPTIMIZATION; PERFORMANCE; DESIGN;
D O I
10.1016/j.dibe.2023.100300
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
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.
引用
收藏
页数:29
相关论文
共 139 条
[1]   Artificial intelligence in the construction industry: A review of present status, opportunities and future challenges [J].
Abioye, Sofiat O. ;
Oyedele, Lukumon O. ;
Akanbi, Lukman ;
Ajayi, Anuoluwapo ;
Delgado, Juan Manuel Davila ;
Bilal, Muhammad ;
Akinade, Olugbenga O. ;
Ahmed, Ashraf .
JOURNAL OF BUILDING ENGINEERING, 2021, 44
[2]   Energy efficiency index as an indicator for measuring building energy performance: A review [J].
Abu Bakar, Nur Najihah ;
Hassan, Mohammad Yusri ;
Abdullah, Hayati ;
Rahman, Hasimah Abdul ;
Abdullah, Md Pauzi ;
Hussin, Faridah ;
Bandi, Masilah .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2015, 44 :1-11
[3]  
Adedara M., 2023, Waste, V1, P389, DOI [10.3390/waste1020024, DOI 10.3390/WASTE1020024]
[4]   Salvaging building materials in a circular economy: A BIM-based whole-life performance estimator [J].
Akanbi, Lukman A. ;
Oyedele, Lukumon O. ;
Akinade, Olugbenga O. ;
Ajayi, Anuoluwapo O. ;
Delgado, Manuel Davila ;
Bilal, Muhammad ;
Bello, Sururah A. .
RESOURCES CONSERVATION AND RECYCLING, 2018, 129 :175-186
[5]   Deep learning in the construction industry: A review of present status and future innovations [J].
Akinosho, Taofeek D. ;
Oyedele, Lukumon O. ;
Bilal, Muhammad ;
Ajayi, Anuoluwapo O. ;
Delgado, Manuel Davila ;
Akinade, Olugbenga O. ;
Ahmed, Ashraf A. .
JOURNAL OF BUILDING ENGINEERING, 2020, 32
[6]   A mixed integer linear programming model and a basic variable neighbourhood search algorithmfor the repatriation scheduling problem [J].
Al-Shihabi, Sameh ;
Mladenovic, Nenad .
EXPERT SYSTEMS WITH APPLICATIONS, 2022, 198
[7]   Building demolition risk assessment by applying a hybrid fuzzy FTA and fuzzy CRITIC-TOPSIS framework [J].
Alipour-Bashary, Milad ;
Ravanshadnia, Mehdi ;
Abbasianjahromi, Hamidreza ;
Asnaashari, Ehsan .
INTERNATIONAL JOURNAL OF BUILDING PATHOLOGY AND ADAPTATION, 2022, 40 (01) :134-159
[8]   Waste Management and Operational Energy for Sustainable Buildings: A Review [J].
Amaral, Rosaria E. C. ;
Brito, Joel ;
Buckman, Matt ;
Drake, Elicia ;
Ilatova, Esther ;
Rice, Paige ;
Sabbagh, Carlos ;
Voronkin, Sergei ;
Abraham, Yewande S. .
SUSTAINABILITY, 2020, 12 (13)
[9]   Transformer machine learning language model for auto-alignment of long-term and short-term plans in construction [J].
Amer, Fouad ;
Jung, Yoonhwa ;
Golparvar-Fard, Mani .
AUTOMATION IN CONSTRUCTION, 2021, 132
[10]   Artificial intelligence in architecture: Generating conceptual design via deep learning [J].
As, Imdat ;
Pal, Siddharth ;
Basu, Prithwish .
INTERNATIONAL JOURNAL OF ARCHITECTURAL COMPUTING, 2018, 16 (04) :306-327