Opportunities and Challenges of Generative AI in Construction Industry: Focusing on Adoption of Text-Based Models
Abstract
:1. Introduction
2. Methodology
3. Various GenAI Model Structures and Related Work in Construction
3.1. Generative Adversarial Network
3.2. Variational AutoEncoders
3.3. Autoregressive Models
3.4. Diffusion Models
3.5. Flow-Based Models
4. Opportunities of GenAI in Construction
4.1. Current GenAI Applications and Developments in Construction
4.2. What Opportunities Are Perceived by Construction Industry Practitioners?
4.3. Potential Applications of GenAI in Construction
4.4. A Conceptual Implementation Framework
5. Challenges of GenAI Implementation in Construction
5.1. Domain Knowledge
5.2. Hallucinations
5.3. Accuracy
5.4. Generalizability
5.5. Model Updates and Interpretability
5.6. Cost
5.7. Ethical Challenges
5.8. Construction Regulatory Challenges
5.9. What Challenges Are Perceived by Construction Industry Practitioners?
- Proactive Approach Needed: The implementation of GenAI in construction requires a proactive approach to security and governance. Addressing these challenges is vital to unlock the potential for improved productivity and creativity during the industry’s technological transformation.
- Strategic Adoption: The adoption of GenAI within construction companies requires a strategic approach to manage security, risks, and governance effectively. The practical procedures allow responsible and ethical utilization while maintaining standards of security, safety, and compliance. The guidance from construction technology experts can support in setting up a successful generative AI program.
- Implementation Challenges: GenAI systems help a comprehensive analysis of trade-offs in construction projects, including physical, financial, and sustainable aspects. However, addressing implementation challenges, such as increasing awareness and understanding, is essential to drive broader adoption and establish convincing business cases for technology investments.
- Limited Awareness: The construction industry is facing difficulties in building an efficient business case for investments in software, hardware, training, and infrastructure due to limited awareness. These challenges related to accessing and sharing big data hinder the effectiveness of GenAI models. Moreover, regulatory and legal complexities, particularly concerning intellectual property rights, add compliance concerns when deploying GenAI in visualizations or renderings.
- Expectation of Mature Technologies: The construction market expects mature technologies ready for immediate use, focusing on solutions designed to the industry’s distinctive challenges. However, this expectation leads to a deeper exploration of automation and AI in construction, recognizing the need for specialized solutions.
- Risk Mitigation and Ethical Governance: To effectively implement GenAI in the construction industry, it is important to apply comprehensive risk mitigation strategies. These include various measures such as data encryption, strict access controls, and secure data storage practices. Furthermore, to safeguard AI-generated outcomes, addressing intellectual property concerns through well-defined guidelines and contractual agreements is essential.
- Novelty Challenge: Another challenge in applying GenAI lies in its novelty. For example, many traditional schedulers are familiar with long-standing tools and may hesitate to embrace newer, more advanced solutions.
6. Recommendations and Future Directions
6.1. Recommendations
- Fine-Tuning LLMs: The recommended initial approach for the integration of GenAI into the construction industry involves the fine-tuning of available powerful pre-trained language models using construction-specific data. Construction companies have the opportunity to curate datasets comprising various resources such as design documents, building codes, contractual documents, technical documents, and BIM data. These data are helpful in informing the selected LLM about specialized vocabulary and contextual nuances of the construction. Starting with modest datasets and focusing on strongly defined tasks can simplify the process of prompt engineering that enables the GenAI systems for construction needs.
- Human Oversight: GenAI systems require human oversight to validate quality and accuracy while capable of automating tasks., or giving ratings to refine LLM outputs, humans directly direct learning closer towards intended needs and quality standards, resulting in improved future. Studies [162,163] indicated that humans have a much richer understanding of context, subtext, culture, and real-world knowledge that LLMs may lack exposure to or have difficulty comprehending. In addition, human feedback provides situational context to enhance the LLMs’ understanding by interactively editing, providing examples, and specifying constraints. Therefore, model outputs should be reviewed, and feedback can be provided to improve performance. Thus, human-in-the-loop approaches that combine AI generation with human judgment can improve the strengths of both.
- Evaluating Business Impact: It is recommended to assess the business impacts of GenAI using experiments measuring key performance indicators. Pilot studies could evaluate model influence on metrics such as productivity, cost, time, risks, etc. The measurement as a model integrates more data and provides insight into returns over investment. This can help to quantify the benefits of GenAI investment for the organization.
- Developing Custom LLMs: In the long run, collaborative efforts between the AEC industry and researchers can focus on designing specialized language model architectures for construction-related tasks. This involves compiling extensive datasets from the AEC domain. The fundamental approach is to establish a secure central data repository, with contributions from construction companies and consultants. Training models on these data, with the support of AI researchers, will allow domain expertise and innovation. However, it is important to understand the challenges including data labelling, computational power, potential biases, overfitting risks, and evaluation difficulties.
6.2. Future Research Directions
- How can we develop GenAI models that can accurately extract detailed project information from a variety of construction documents and BIM models? This could help improve productivity.
- What techniques can enable GenAI models to automatically generate feasible building designs based on requirements? Generative design could help with time and cost savings.
- How can we build AI assistants that can have natural conversations with human stakeholders to refine project details, requirements, and reports in different phases of the building lifecycle? Conversational AI could help project stakeholders.
- What GenAI techniques can enable the automated generation of 3D visualizations, videos, and images from text descriptions? This could help in better communication.
- How can we develop AI systems to accurately evaluate construction progress, safety, and quality using visual data? Computer vision integration could be key to achieving this.
- What GenAI techniques can optimize construction scheduling, logistics, and cost estimating? This could help in construction project management.
- How can we build AI assistants that can understand BIM model information, extract that information, and update BIM models based on prompts? This could help to accelerate the BIM execution process for general contractors.
- How can we integrate robotics with natural language AI to enable easy human-robot interactions? Future studies on challenges and methodology development are recommended. This could help improve the usability, and accessibility of robotic systems, leading to improved collaboration.
- What machine learning techniques can support accurate automatic code generation for construction tasks and changes in scope? This could help to track changes and troubleshoot issues.
- How can we build GenAI models that learn continuously from construction data to improve predictions and decision-making over time? Further exploration studies on adaptive algorithms, LLMs learning frameworks, and other relevant methodologies to improve the continuous learning aspect of GenAI models within the construction domain is recommended. This could help in the overall success of an organization, and future project forecasting.
7. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Xu, Y.; Zhou, Y.; Sekula, P.; Ding, L. Machine learning in construction: From shallow to deep learning. Dev. Built Environ. 2021, 6, 100045. [Google Scholar] [CrossRef]
- Liu, Y.; Yang, Z.; Yu, Z.; Liu, Z.; Liu, D.; Lin, H.; Li, M.; Ma, S.; Avdeev, M.; Shi, S. Generative artificial intelligence and its applications in materials science: Current situation and future perspectives. J. Mater. 2023, 9, 798–816. [Google Scholar] [CrossRef]
- Duan, Y.; Edwards, J.S.; Dwivedi, Y.K. Artificial intelligence for decision making in the era of Big Data–Evolution, challenges and research agenda. Int. J. Inf. Manag. 2019, 48, 63–71. [Google Scholar] [CrossRef]
- Abioye, S.O.; Oyedele, L.O.; Akanbi, L.; Ajayi, A.; Davila Delgado, J.M.; Bilal, M.; Akinade, O.O.; Ahmed, A. Artificial intelligence in the construction industry: A review of present status, opportunities and future challenges. J. Build. Eng. 2021, 44, 103299. [Google Scholar] [CrossRef]
- Baidoo-Anu, D.; Owusu Ansah, L. Education in the Era of Generative Artificial Intelligence (AI): Understanding the Potential Benefits of ChatGPT in Promoting Teaching and Learning. J. AI 2023, 7, 52–62. [Google Scholar] [CrossRef]
- Qwiklabs. Introduction to Generative AI. Google Cloud Skills Boost. Available online: https://www.cloudskillsboost.google/course_sessions/4093050/video/384243 (accessed on 16 August 2023).
- Li, C.; Su, Y.; Liu, W. Text-To-Text Generative Adversarial Networks. In Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil, 8–13 July 2018; pp. 1–7. [Google Scholar]
- Zhang, C.; Zhang, C.; Zhang, M.; Kweon, I.S. Text-to-image Diffusion Models in Generative AI: A Survey. arXiv 2023, arXiv:2303.07909. [Google Scholar]
- Liu, V.; Long, T.; Raw, N.; Chilton, L. Generative Disco: Text-to-Video Generation for Music Visualization. arXiv 2023, arXiv:2304.08551. [Google Scholar]
- Lei, T.; Barzilay, R.; Jaakkola, T. Rationalizing Neural Predictions. arXiv 2016, arXiv:1606.04155. [Google Scholar]
- Gozalo-Brizuela, R.; Garrido-Merchan, E.C. ChatGPT is not all you need. A State of the Art Review of large Generative AI models. arXiv 2023, arXiv:2301.04655. [Google Scholar]
- Poh, C.Q.X.; Ubeynarayana, C.U.; Goh, Y.M. Safety leading indicators for construction sites: A machine learning approach. Autom. Constr. 2018, 93, 375–386. [Google Scholar] [CrossRef]
- Goh, Y.M.; Chua, D. Neural network analysis of construction safety management systems: A case study in Singapore. Constr. Manag. Econ. 2013, 31, 460–470. [Google Scholar] [CrossRef]
- Chua, D.K.; Goh, Y.M. Poisson Model of Construction Incident Occurrence. J. Constr. Eng. Manag. 2005, 131, 715–722. [Google Scholar] [CrossRef]
- Fang, W.; Ding, L.; Love, P.E.D.; Luo, H.; Li, H.; Peña-Mora, F.; Zhong, B.; Zhou, C. Computer vision applications in construction safety assurance. Autom. Constr. 2020, 110, 103013. [Google Scholar] [CrossRef]
- Liu, J.; Luo, H.; Liu, H. Deep learning-based data analytics for safety in construction. Autom. Constr. 2022, 140, 104302. [Google Scholar] [CrossRef]
- Paneru, S.; Jeelani, I. Computer vision applications in construction: Current state, opportunities & challenges. Autom. Constr. 2021, 132, 103940. [Google Scholar] [CrossRef]
- Williams, T.P.; Gong, J. Predicting construction cost overruns using text mining, numerical data and ensemble classifiers. Autom. Constr. 2014, 43, 23–29. [Google Scholar] [CrossRef]
- Cheng, M.-Y.; Tsai, H.-C.; Hsieh, W.-S. Web-based conceptual cost estimates for construction projects using Evolutionary Fuzzy Neural Inference Model. Autom. Constr. 2009, 18, 164–172. [Google Scholar] [CrossRef]
- Ghimire, P.; Pokharel, S.; Kim, K.; Barutha, P. Machine learning-based prediction models for budget forecast in capital construction. In Proceedings of the 2nd International Conference on Construction, Energy, Environment & Sustainability, Funchal, Portugal, 27–30 June 2023. [Google Scholar]
- Baduge, S.K.; Thilakarathna, S.; Perera, J.S.; Arashpour, M.; Sharafi, P.; Teodosio, B.; Shringi, A.; Mendis, P. Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications. Autom. Constr. 2022, 141, 104440. [Google Scholar] [CrossRef]
- Mahmoodzadeh, A.; Nejati, H.R.; Mohammadi, M. Optimized machine learning modelling for predicting the construction cost and duration of tunnelling projects. Autom. Constr. 2022, 139, 104305. [Google Scholar] [CrossRef]
- Zhang, C.; Kuppannagari, S.R.; Kannan, R.; Prasanna, V.K. Building HVAC Scheduling Using Reinforcement Learning via Neural Network Based Model Approximation. In Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation; Association for Computing Machinery, in BuildSys ’19, New York, NY, USA, 13–14 November 2019; pp. 287–296. [Google Scholar]
- Hatami, M.; Franz, B.; Paneru, S.; Flood, I. Using Deep Learning Artificial Intelligence to Improve Foresight Method in the Optimization of Planning and Scheduling of Construction Processes. In Computing in Civil Engineering 2021; ASCE: Reston, VI, USA, 2022; pp. 1171–1178. [Google Scholar] [CrossRef]
- Chen, J.; Fang, Y.; Cho, Y.K.; Kim, C. Principal Axes Descriptor for Automated Construction-Equipment Classification from Point Clouds. J. Comput. Civ. Eng. 2017, 31, 04016058. [Google Scholar] [CrossRef]
- Sakhakarmi, S.; Park, J.; Cho, C. Enhanced Machine Learning Classification Accuracy for Scaffolding Safety Using Increased Features. J. Constr. Eng. Manag. 2019, 145, 04018133. [Google Scholar] [CrossRef]
- Teizer, J. Status quo and open challenges in vision-based sensing and tracking of temporary resources on infrastructure construction sites. Adv. Eng. Inform. 2015, 29, 225–238. [Google Scholar] [CrossRef]
- Zhu, Z.; Brilakis, I. Parameter optimization for automated concrete detection in image data. Autom. Constr. 2010, 19, 944–953. [Google Scholar] [CrossRef]
- Pour Rahimian, F.; Seyedzadeh, S.; Oliver, S.; Rodriguez, S.; Dawood, N. On-demand monitoring of construction projects through a game-like hybrid application of BIM and machine learning. Autom. Constr. 2020, 110, 103012. [Google Scholar] [CrossRef]
- Andenæs, E.; Engebø, A.; Time, B.; Lohne, J.; Torp, O.; Kvande, T. Perspectives on Quality Risk in the Building Process of Blue-Green Roofs in Norway. Buildings 2020, 10, 189. [Google Scholar] [CrossRef]
- Saravanan, V.; Pourhomayoun, M.; Mazari, M. A Proposed Method to Improve Higway Construction Quality Using Machine Learning. In Proceedings of the 2018 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA, 12–14 December 2018; pp. 1218–1221. [Google Scholar]
- Sacks, R.; Girolami, M.; Brilakis, I. Building Information Modelling, Artificial Intelligence and Construction Tech. Dev. Built Environ. 2020, 4, 100011. [Google Scholar] [CrossRef]
- Al Qady, M.; Kandil, A. Concept Relation Extraction from Construction Documents Using Natural Language Processing. J. Constr. Eng. Manag. 2010, 136, 294–302. [Google Scholar] [CrossRef]
- Bloch, T.; Sacks, R. Comparing machine learning and rule-based inferencing for semantic enrichment of BIM models. Autom. Constr. 2018, 91, 256–272. [Google Scholar] [CrossRef]
- Pournader, M.; Ghaderi, H.; Hassanzadegan, A.; Fahimnia, B. Artificial intelligence applications in supply chain management. Int. J. Prod. Econ. 2021, 241, 108250. [Google Scholar] [CrossRef]
- Hatami, M.; Paneru, S.; Flood, I. Applicability of Artificial Intelligence (AI) Methods to Construction Manufacturing: A Literature Review. In Construction Research Congress 2022; ASCE: Reston, VI, USA, 2022; pp. 1298–1306. [Google Scholar] [CrossRef]
- Choudhari, S.; Tindwani, A. Logistics optimisation in road construction project. Constr. Innov. 2017, 17, 158–179. [Google Scholar] [CrossRef]
- Fang, Y.; Ng, S.T. Genetic algorithm for determining the construction logistics of precast components. Eng. Constr. Archit. Manag. 2019, 26, 2289–2306. [Google Scholar] [CrossRef]
- Darko, A.; Chan, A.P.C.; Adabre, M.A.; Edwards, D.J.; Hosseini, M.R.; Ameyaw, E.E.; Ameyaw, E.E. Artificial intelligence in the AEC industry: Scientometric analysis and visualization of research activities. Autom. Constr. 2020, 112, 103081. [Google Scholar] [CrossRef]
- Yaseen, Z.M.; Ali, Z.H.; Salih, S.Q.; Al-Ansari, N. Prediction of Risk Delay in Construction Projects Using a Hybrid Artificial Intelligence Model. Sustainability 2020, 12, 1514. [Google Scholar] [CrossRef]
- Sanni-Anibire, M.O.; Zin, R.M.; Olatunji, S.O. Machine learning model for delay risk assessment in tall building projects. Int. J. Constr. Manag. 2022, 22, 2134–2143. [Google Scholar] [CrossRef]
- Pokharel, S.; Roy, T.; Admiraal, D. Effects of mass balance, energy balance, and storage-discharge constraints on LSTM for streamflow prediction. Environ. Model. Softw. 2023, 166, 105730. [Google Scholar] [CrossRef]
- Afzal, F.; Yunfei, S.; Nazir, M.; Bhatti, S.M. A review of artificial intelligence based risk assessment methods for capturing complexity-risk interdependencies: Cost overrun in construction projects. Int. J. Manag. Proj. Bus. 2019, 14, 300–328. [Google Scholar] [CrossRef]
- Lin, S.-S.; Shen, S.-L.; Zhou, A.; Xu, Y.-S. Risk assessment and management of excavation system based on fuzzy set theory and machine learning methods. Autom. Constr. 2021, 122, 103490. [Google Scholar] [CrossRef]
- Chen, J.-H. KNN based knowledge-sharing model for severe change order disputes in construction. Autom. Constr. 2008, 17, 773–779. [Google Scholar] [CrossRef]
- Chou, J.-S.; Lin, C. Predicting Disputes in Public-Private Partnership Projects: Classification and Ensemble Models. J. Comput. Civ. Eng. 2013, 27, 51–60. [Google Scholar] [CrossRef]
- Lu, W.; Lou, J.; Webster, C.; Xue, F.; Bao, Z.; Chi, B. Estimating construction waste generation in the Greater Bay Area, China using machine learning. Waste Manag. 2021, 134, 78–88. [Google Scholar] [CrossRef]
- Coskuner, G.; Jassim, M.S.; Zontul, M.; Karateke, S. Application of artificial intelligence neural network modeling to predict the generation of domestic, commercial and construction wastes. Waste Manag. Res. 2021, 39, 499–507. [Google Scholar] [CrossRef] [PubMed]
- Yu, K.H.; Zhang, Y.; Li, D.; Montenegro-Marin, C.E.; Kumar, P.M. Environmental planning based on reduce, reuse, recycle and recover using artificial intelligence. Environ. Impact Assess. Rev. 2021, 86, 106492. [Google Scholar] [CrossRef]
- Mehmood, M.U.; Chun, D.; Zeeshan; Han, H.; Jeon, G.; Chen, K. A review of the applications of artificial intelligence and big data to buildings for energy-efficiency and a comfortable indoor living environment. Energy Build. 2019, 202, 109383. [Google Scholar] [CrossRef]
- Fathi, S.; Srinivasan, R.; Fenner, A.; Fathi, S. Machine learning applications in urban building energy performance forecasting: A systematic review. Renew. Sustain. Energy Rev. 2020, 133, 110287. [Google Scholar] [CrossRef]
- Nasruddin; Sholahudin; Satrio, P.; Mahlia, T.M.I.; Giannetti, N.; Saito, K. Optimization of HVAC system energy consumption in a building using artificial neural network and multi-objective genetic algorithm. Sustain. Energy Technol. Assess. 2019, 35, 48–57. [Google Scholar] [CrossRef]
- Debrah, C.; Chan, A.P.C.; Darko, A. Artificial intelligence in green building. Autom. Constr. 2022, 137, 104192. [Google Scholar] [CrossRef]
- Kar, A.K.; Choudhary, S.K.; Singh, V.K. How can artificial intelligence impact sustainability: A systematic literature review. J. Clean. Prod. 2022, 376, 134120. [Google Scholar] [CrossRef]
- Seo, J.; Park, H.; Choo, S. Inference of Drawing Elements and Space Usage on Architectural Drawings Using Semantic Segmentation. Appl. Sci. 2020, 10, 7347. [Google Scholar] [CrossRef]
- Tan, K. The Framework of Combining Artificial Intelligence and Construction 3D Printing in Civil Engineering. MATEC Web Conf. 2018, 206, 01008. [Google Scholar] [CrossRef]
- Pantoja-Rosero, B.G.; Oner, D.; Kozinski, M.; Achanta, R.; Fua, P.; Perez-Cruz, F.; Beyer, K. TOPO-Loss for continuity-preserving crack detection using deep learning. Constr. Build. Mater. 2022, 344, 128264. [Google Scholar] [CrossRef]
- Spencer, B.F.; Hoskere, V.; Narazaki, Y. Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring. Engineering 2019, 5, 199–222. [Google Scholar] [CrossRef]
- Hoskere, V.; Narazaki, Y.; Hoang, T.; Spencer, B., Jr. Vision-based Structural Inspection using Multiscale Deep Convolutional Neural Networks. arXiv 2018, arXiv:1805.01055. [Google Scholar] [CrossRef]
- Narazaki, Y.; Hoskere, V.; Yoshida, K.; Spencer, B.F.; Fujino, Y. Synthetic environments for vision-based structural condition assessment of Japanese high-speed railway viaducts. Mech. Syst. Signal Process. 2021, 160, 107850. [Google Scholar] [CrossRef]
- Van, T.N.; Quoc, T.N. Research Trends on Machine Learning in Construction Management: A Scientometric Analysis. J. Appl. Sci. Technol. Trends 2021, 2, 96–104. [Google Scholar] [CrossRef]
- Cao, Y.; Ashuri, B. Predicting the Volatility of Highway Construction Cost Index Using Long Short-Term Memory. J. Manag. Eng. 2020, 36, 04020020. [Google Scholar] [CrossRef]
- Semaan, N.; Salem, M. A deterministic contractor selection decision support system for competitive bidding. Eng. Constr. Archit. Manag. 2017, 24, 61–77. [Google Scholar] [CrossRef]
- Liu, C.; ME Sepasgozar, S.; Sepasgozar, S.; Shirowzhan, S.; Mohammadi, G. Applications of object detection in modular construction based on a comparative evaluation of deep learning algorithms. Constr. Innov. 2021, 22, 141–159. [Google Scholar] [CrossRef]
- Zabin, A.; González, V.A.; Zou, Y.; Amor, R. Applications of machine learning to BIM: A systematic literature review. Adv. Eng. Inform. 2022, 51, 101474. [Google Scholar] [CrossRef]
- Kim, J.; Liu, J.; Ghimire, P. The Categorization of Virtual Design and Construction Services. In Proceedings of the 2019 International Council for Research and Innovation in Building and Construction–CIB World Building Congress, Hong Kong, China, 17–21 June 2019. [Google Scholar]
- Mulero-Palencia, S.; Álvarez-Díaz, S.; Andrés-Chicote, M. Machine Learning for the Improvement of Deep Renovation Building Projects Using As-Built BIM Models. Sustainability 2021, 13, 6576. [Google Scholar] [CrossRef]
- Paneru, S.; Ghimire, P.; Kandel, A.; Thapa, S.; Koirala, N.; Karki, M. An Exploratory Investigation of Implementation of Building Information Modeling in Nepalese Architecture–Engineering–Construction Industry. Buildings 2023, 13, 552. [Google Scholar] [CrossRef]
- Bassir, D.; Lodge, H.; Chang, H.; Majak, J.; Chen, G. Application of artificial intelligence and machine learning for BIM: Review. Int. J. Simul. Multidiscip. Des. Optim. 2023, 14, 5. [Google Scholar] [CrossRef]
- Pan, M.; Yang, Y.; Zheng, Z.; Pan, W. Artificial Intelligence and Robotics for Prefabricated and Modular Construction: A Systematic Literature Review. J. Constr. Eng. Manag. 2022, 148, 03122004. [Google Scholar] [CrossRef]
- You, K.; Zhou, C.; Ding, L. Deep learning technology for construction machinery and robotics. Autom. Constr. 2023, 150, 104852. [Google Scholar] [CrossRef]
- Bock, T. Construction robotics. Auton. Robot. 2007, 22, 201–209. [Google Scholar] [CrossRef]
- Oyediran, H.; Ghimire, P.; Peavy, M.; Kim, K.; Barutha, P. Robotics Applicability for Routine Operator Tasks in Power Plant Facilities. In Proceedings of the International Symposium on Automation and Robotics in Construction, Dubai, United Arab Emirates, 1–5 November 2021. [Google Scholar]
- Meskó, B.; Topol, E.J. The imperative for regulatory oversight of large language models (or generative AI) in healthcare. Npj Digit. Med. 2023, 6, 120. [Google Scholar] [CrossRef] [PubMed]
- Dogru, T.; Line, N.; Mody, M.; Hanks, L.; Abbott, J.; Acikgoz, F.; Assaf, A.; Bakir, S.; Berbekova, A.; Bilgihan, A.; et al. Generative Artificial Intelligence in the Hospitality and Tourism Industry: Developing a Framework for Future Research. J. Hosp. Tour. Res. 2023, 10963480231188664. [Google Scholar] [CrossRef]
- Dwivedi, Y.K.; Kshetri, N.; Hughes, L.; Slade, E.L.; Jeyaraj, A.; Kar, A.K.; Baabdullah, A.M.; Koohang, A.; Raghavan, V.; Ahuja, M.; et al. Opinion Paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. Int. J. Inf. Manag. 2023, 71, 102642. [Google Scholar] [CrossRef]
- Fui-Hoon Nah, F.; Zheng, R.; Cai, J.; Siau, K.; Chen, L. Generative AI and ChatGPT: Applications, challenges, and AI-human collaboration. J. Inf. Technol. Case Appl. Res. 2023, 25, 277–304. [Google Scholar] [CrossRef]
- Kammoun, A.; Slama, R.; Tabia, H.; Ouni, T.; Abid, M. Generative Adversarial Networks for Face Generation: A Survey. ACM Comput. Surv. 2022, 55, 1–37. [Google Scholar] [CrossRef]
- Wu, A.N.; Stouffs, R.; Biljecki, F. Generative Adversarial Networks in the built environment: A comprehensive review of the application of GANs across data types and scales. Build. Environ. 2022, 223, 109477. [Google Scholar] [CrossRef]
- Goodfellow, I.; Pouget-Abadie, J.; Mirza, M.; Xu, B.; Warde-Farley, D.; Ozair, S.; Courville, A.; Bengio, Y. Generative adversarial networks. Commun. ACM 2020, 63, 139–144. [Google Scholar] [CrossRef]
- You, A.; Kim, J.K.; Ryu, I.H.; Yoo, T.K. Application of generative adversarial networks (GAN) for ophthalmology image domains: A survey. Eye Vis. 2022, 9, 6. [Google Scholar] [CrossRef]
- Wang, K.; Gou, C.; Duan, Y.; Lin, Y.; Zheng, X.; Wang, F.-Y. Generative adversarial networks: Introduction and outlook. IEEECAA J. Autom. Sin. 2017, 4, 588–598. [Google Scholar] [CrossRef]
- Chokwitthaya, C.; Collier, E.; Zhu, Y.; Mukhopadhyay, S. Improving Prediction Accuracy in Building Performance Models Using Generative Adversarial Networks (GANs). arXiv 2019, arXiv:1906.05767. [Google Scholar]
- Doersch, C. Tutorial on Variational Autoencoders. arXiv 2021, arXiv:1606.05908. [Google Scholar] [CrossRef]
- Kingma, D.P.; Welling, M. Auto-Encoding Variational Bayes. arXiv 2022, arXiv:1312.6114. [Google Scholar] [CrossRef]
- Kingma, D.P.; Welling, M. An Introduction to Variational Autoencoders. Found. TrendsMach. Learn. 2019, 12, 307–392. [Google Scholar] [CrossRef]
- Yang, Y.; Zheng, K.; Wu, C.; Yang, Y. Improving the Classification Effectiveness of Intrusion Detection by Using Improved Conditional Variational AutoEncoder and Deep Neural Network. Sensors 2019, 19, 2528. [Google Scholar] [CrossRef]
- Bond-Taylor, S.; Leach, A.; Long, Y.; Willcocks, C.G. Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models. IEEE Trans. Pattern Anal. Mach. Intell. 2022, 44, 7327–7347. [Google Scholar] [CrossRef]
- Huang, D.; Song, X.; Fan, Z.; Jiang, R.; Shibasaki, R.; Zhang, Y.; Wang, H.; Kato, Y. A Variational Autoencoder Based Generative Model of Urban Human Mobility. In Proceedings of the 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), San Jose, CA, USA, 28–30 March 2019; pp. 425–430. [Google Scholar]
- Davila Delgado, J.M.; Oyedele, L. Deep learning with small datasets: Using autoencoders to address limited datasets in construction management. Appl. Soft Comput. 2021, 112, 107836. [Google Scholar] [CrossRef]
- Balmer, V.M.; Kuhn, S.V.; Bischof, R.; Salamanca, L.; Kaufmann, W.; Perez-Cruz, F.; Kraus, M.A. Design Space Exploration and Explanation via Conditional Variational Autoencoders in Meta-model-based Conceptual Design of Pedestrian Bridges. arXiv 2022, arXiv:2211.16406. [Google Scholar] [CrossRef]
- Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A.N.; Kaiser, Ł.; Polosukhin, I. Attention is All you Need. In Advances in Neural Information Processing Systems; Curran Associates, Inc.: Red Hook, NY, USA, 2017; Volume 30, Available online: https://proceedings.neurips.cc/paper_files/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html (accessed on 11 September 2023).
- Autoregressive Models in Deep Learning—A Brief Survey. Available online: https://www.georgeho.org/deep-autoregressive-models/ (accessed on 11 September 2023).
- Bengio, Y.; Ducharme, R.; Vincent, P. A Neural Probabilistic Language Model. In Advances in Neural Information Processing Systems; MIT Press: Cambridge, MA, USA, 2000; Volume 13, Available online: https://proceedings.neurips.cc/paper_files/paper/2000/hash/728f206c2a01bf572b5940d7d9a8fa4c-Abstract.html (accessed on 11 September 2023).
- Elfahham, Y. Estimation and prediction of construction cost index using neural networks, time series, and regression. Alex. Eng. J. 2019, 58, 499–506. [Google Scholar] [CrossRef]
- Wu, T.; Fan, Z.; Liu, X.; Gong, Y.; Shen, Y.; Jiao, J.; Zheng, H.-T.; Li, J.; Wei, Z.; Guo, J.; et al. AR-Diffusion: Auto-Regressive Diffusion Model for Text Generation. arXiv 2023, arXiv:2305.09515. [Google Scholar] [CrossRef]
- Schneider, F. ArchiSound: Audio Generation with Diffusion. arXiv 2023, arXiv:2301.13267. [Google Scholar] [CrossRef]
- Yuan, Y.; Song, J.; Iqbal, U.; Vahdat, A.; Kautz, J. PhysDiff: Physics-Guided Human Motion Diffusion Model. In Proceedings of the IEEE/CVF International Conference on Computer Vision, Paris, France, 2–3 October 2023; pp. 16010–16021. Available online: https://openaccess.thecvf.com/content/ICCV2023/html/Yuan_PhysDiff_Physics-Guided_Human_Motion_Diffusion_Model_ICCV_2023_paper.html (accessed on 4 December 2023).
- Weng, L. What are Diffusion Models? Available online: https://lilianweng.github.io/posts/2021-07-11-diffusion-models/ (accessed on 16 September 2023).
- Ho, J.; Jain, A.; Abbeel, P. Denoising Diffusion Probabilistic Models. In Advances in Neural Information Processing Systems; Curran Associates, Inc.: Red Hook, NY, USA, 2020; Volume 33, pp. 6840–6851. Available online: https://proceedings.neurips.cc/paper/2020/hash/4c5bcfec8584af0d967f1ab10179ca4b-Abstract.html (accessed on 16 September 2023).
- Kazerouni, A.; Aghdam, E.K.; Heidari, M.; Azad, R.; Fayyaz, M.; Hacihaliloglu, I.; Merhof, D. Diffusion Models for Medical Image Analysis: A Comprehensive Survey. arXiv 2023, arXiv:2211.07804. [Google Scholar] [CrossRef]
- Croitoru, F.-A.; Hondru, V.; Ionescu, R.T.; Shah, M. Diffusion Models in Vision: A Survey. IEEE Trans. Pattern Anal. Mach. Intell. 2023, 45, 10850–10869. [Google Scholar] [CrossRef] [PubMed]
- Chen, J.; Shao, Z.; Hu, B. Generating Interior Design from Text: A New Diffusion Model-Based Method for Efficient Creative Design. Buildings 2023, 13, 1861. [Google Scholar] [CrossRef]
- Survey of Generative AI in Architecture and Design-ProQuest. Available online: https://www.proquest.com/openview/3e20f970d5beb0885f4488584bf9ae5b/1?pq-origsite=gscholar&cbl=18750&diss=y (accessed on 3 December 2023).
- Mishra, S.; Mishra, M.; Kim, T.; Har, D. Road Redesign Technique Achieving Enhanced Road Safety by Inpainting with a Diffusion Model. arXiv 2023, arXiv:2302.07440. [Google Scholar] [CrossRef]
- Ploennigs, J.; Berger, M. Diffusion Models for Computational Design at the Example of Floor Plans. arXiv 2023, arXiv:2307.02511. [Google Scholar]
- Weng, L. Flow-Based Deep Generative Models. Available online: https://lilianweng.github.io/posts/2018-10-13-flow-models/ (accessed on 16 September 2023).
- Dinh, L.; Krueger, D.; Bengio, Y. NICE: Non-Linear Independent Components Estimation. arXiv 2015, arXiv:1410.8516v6. [Google Scholar]
- Kumar, M.; Babaeizadeh, M.; Erhan, D.; Finn, C.; Levine, S.; Dinh, L.; Kingma, D. VideoFlow: A Flow-Based Generative Model for Video. arXiv 2019, arXiv:1903.01434. [Google Scholar]
- Dinh, L.; Sohl-Dickstein, J.; Bengio, S. Density estimation using Real NVP. arXiv 2017, arXiv:1605.08803v3. [Google Scholar]
- Mo, Z.; Fu, Y.; Xu, D.; Di, X. TrafficFlowGAN: Physics-Informed Flow Based Generative Adversarial Network for Uncertainty Quantification. In Machine Learning and Knowledge Discovery in Databases; Amini, M.-R., Canu, S., Fischer, A., Guns, T., Kralj Novak, P., Tsoumakas, G., Eds.; Springer Nature Switzerland: Cham, Switzerland, 2023; pp. 323–339. [Google Scholar]
- Lee, J.; Kim, H.; Shim, J.; Hwang, E. Cartoon-Flow: A Flow-Based Generative Adversarial Network for Arbitrary-Style Photo Cartoonization. In Proceedings of the 30th ACM International Conference on Multimedia, Lisbon, Portugal, 10–14 October 2022; Association for Computing Machinery: New York, NY, USA, 2022; pp. 1241–1251. [Google Scholar]
- Zheng, J.; Fischer, M. BIM-GPT: A Prompt-Based Virtual Assistant Framework for BIM Information Retrieval. arXiv 2023, arXiv:2304.09333. [Google Scholar]
- Jang, S.; Lee, G. Interactive Design by Integrating a Large Pre-Trained Language Model and Building Information Modeling. arXiv 2023, arXiv:2306.14165. [Google Scholar] [CrossRef]
- Zheng, J.; Fischer, M. Dynamic prompt-based virtual assistant framework for BIM information search. Autom. Constr. 2023, 155, 105067. [Google Scholar] [CrossRef]
- Prieto, S.A.; Mengiste, E.T.; García de Soto, B. Investigating the Use of ChatGPT for the Scheduling of Construction Projects. Buildings 2023, 13, 857. [Google Scholar] [CrossRef]
- Mohamed Hassan, H.A.; Marengo, E.; Nutt, W. A BERT-Based Model for Question Answering on Construction Incident Reports. In Natural Language Processing and Information Systems; Rosso, P., Basile, V., Martínez, R., Métais, E., Meziane, F., Eds.; Springer International Publishing: Cham, Switzerland, 2022; pp. 215–223. [Google Scholar]
- Moon, S.; Chi, S.; Im, S.-B. Automated detection of contractual risk clauses from construction specifications using bidirectional encoder representations from transformers (BERT). Autom. Constr. 2022, 142, 104465. [Google Scholar] [CrossRef]
- Chung, S.; Moon, S.; Kim, J.; Kim, J.; Lim, S.; Chi, S. Comparing natural language processing (NLP) applications in construction and computer science using preferred reporting items for systematic reviews (PRISMA). Autom. Constr. 2023, 154, 105020. [Google Scholar] [CrossRef]
- You, H.; Ye, Y.; Zhou, T.; Zhu, Q.; Du, J. Robot-Enabled Construction Assembly with Automated Sequence Planning based on ChatGPT: RoboGPT. arXiv 2023. [Google Scholar] [CrossRef]
- Xie, Y.; Yu, C.; Zhu, T.; Bai, J.; Gong, Z.; Soh, H. Translating Natural Language to Planning Goals with Large-Language Models. arXiv 2023, arXiv:2302.05128. [Google Scholar]
- Guan, L.; Valmeekam, K.; Sreedharan, S.; Kambhampati, S. Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning. arXiv 2023, arXiv:2305.14909. [Google Scholar]
- AI Caucus Leaders Introduce Bipartisan Bill to Expand Access to AI Research. Available online: http://eshoo.house.gov/media/press-releases/ai-caucus-leaders-introduce-bipartisan-bill-expand-access-ai-research (accessed on 16 August 2023).
- Floridi, L.; Chiriatti, M. GPT-3: Its Nature, Scope, Limits, and Consequences. Minds Mach. 2020, 30, 681–694. [Google Scholar] [CrossRef]
- GPT-3. Wikipedia. 13 August 2023. Available online: https://en.wikipedia.org/w/index.php?title=GPT-3&oldid=1170092033 (accessed on 26 August 2023).
- GPT-4. Available online: https://openai.com/gpt-4 (accessed on 26 August 2023).
- Chowdhery, A.; Narang, S.; Devlin, J.; Bosma, M.; Mishra, G.; Roberts, A.; Barham, P.; Chung, H.W.; Sutton, C.; Gehrmann, S.; et al. PaLM: Scaling Language Modeling with Pathways. arXiv 2022, arXiv:2204.02311. [Google Scholar]
- Google AI PaLM 2–Google AI. Available online: https://ai.google/discover/palm2/ (accessed on 26 August 2023).
- Touvron, H.; Lavril, T.; Izacard, G.; Martinet, X.; Lachaux, M.-A.; Lacroix, T.; Rozière, B.; Goyal, N.; Hambro, E.; Azhar, F.; et al. LLaMA: Open and Efficient Foundation Language Models. arXiv 2023, arXiv:2302.13971. [Google Scholar]
- Zhang, S.; Roller, S.; Goyal, N.; Artetxe, M.; Chen, M.; Chen, S.; Dewan, C.; Diab, M.; Li, X.; Lin, X.V.; et al. OPT: Open Pre-trained Transformer Language Models. arXiv 2022, arXiv:2205.01068. [Google Scholar]
- Sha, A. 12 Best Large Language Models (LLMs) in 2023. Available online: https://beebom.com/best-large-language-models-llms/ (accessed on 26 August 2023).
- Akepanidtaworn, K. Data Behind the Large Language Models (LLM), GPT, and Beyond. Medium 2023. Available online: https://kyleake.medium.com/data-behind-the-large-language-models-llm-gpt-and-beyond-8b34f508b5de (accessed on 26 August 2023).
- Heimerl, F.; Lohmann, S.; Lange, S.; Ertl, T. Word Cloud Explorer: Text Analytics Based on Word Clouds. In Proceedings of the 2014 47th Hawaii International Conference on System Sciences, Waikoloa, HI, USA, 6–9 January 2014; pp. 1833–1842. [Google Scholar]
- Kabir, A.I.; AHMED, K.; Karim, R. Word Cloud and Sentiment Analysis of Amazon Earphones Reviews with R Programming Language. Inform. Econ. 2020, 24, 55–71. [Google Scholar] [CrossRef]
- NLTK: Natural Language Toolkit. Available online: https://www.nltk.org/ (accessed on 4 December 2023).
- Vencer, L.V.T.; Bansa, H.; Caballero, A.R. Data and Sentiment Analysis of Monkeypox Tweets using Natural Language Toolkit (NLTK). In Proceedings of the 2023 8th International Conference on Business and Industrial Research (ICBIR), Bangkok, Thailand, 18–19 May 2023; pp. 392–396. [Google Scholar]
- Thanaki, J. Python Natural Language Processing; Packt Publishing Ltd.: Birmingham, UK, 2017; ISBN 978-1-78728-552-1. [Google Scholar]
- Birjali, M.; Kasri, M.; Beni-Hssane, A. A comprehensive survey on sentiment analysis: Approaches, challenges and trends. Knowl.-Based Syst. 2021, 226, 107134. [Google Scholar] [CrossRef]
- Xu, G.; Meng, Y.; Qiu, X.; Yu, Z.; Wu, X. Sentiment Analysis of Comment Texts Based on BiLSTM. IEEE Access 2019, 7, 51522–51532. [Google Scholar] [CrossRef]
- Medhat, W.; Hassan, A.; Korashy, H. Sentiment analysis algorithms and applications: A survey. Ain Shams Eng. J. 2014, 5, 1093–1113. [Google Scholar] [CrossRef]
- TextBlob: Simplified Text Processing—TextBlob 0.16.0 Documentation. Available online: https://textblob.readthedocs.io/en/dev/ (accessed on 30 August 2023).
- Xu, X.; Ma, L.; Ding, L. A Framework for BIM-Enabled Life-Cycle Information Management of Construction Project. Int. J. Adv. Robot. Syst. 2014, 11, 126. [Google Scholar] [CrossRef]
- Hu, W. Information Lifecycle Modeling Framework for Construction Project Lifecycle Management. In Proceedings of the 2008 International Seminar on Future Information Technology and Management Engineering, Leicestershire, UK, 20 November 2008; pp. 372–375. [Google Scholar]
- Succar, B. Building information modelling framework: A research and delivery foundation for industry stakeholders. Autom. Constr. 2009, 18, 357–375. [Google Scholar] [CrossRef]
- Becerik-Gerber, B.; Jazizadeh, F.; Li, N.; Calis, G. Application Areas and Data Requirements for BIM-Enabled Facilities Management. J. Constr. Eng. Manag. 2012, 138, 431–442. [Google Scholar] [CrossRef]
- Finetuning Large Language Models-DeepLearning.AI. Available online: https://www.deeplearning.ai/short-courses/finetuning-large-language-models/ (accessed on 18 October 2023).
- Kuang, W.; Qian, B.; Li, Z.; Chen, D.; Gao, D.; Pan, X.; Xie, Y.; Li, Y.; Ding, B.; Zhou, J. FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning. arXiv 2023, arXiv:2309.00363. [Google Scholar] [CrossRef]
- Ji, Z.; Lee, N.; Frieske, R.; Yu, T.; Su, D.; Xu, Y.; Ishii, E.; Bang, Y.J.; Madotto, A.; Fung, P. Survey of Hallucination in Natural Language Generation. ACM Comput. Surv. 2023, 55, 1–38. [Google Scholar] [CrossRef]
- Saka, A.; Taiwo, R.; Saka, N.; Salami, B.; Ajayi, S.; Akande, K.; Kazemi, H. GPT Models in Construction Industry: Opportunities, Limitations, and a Use Case Validation. arXiv 2023, arXiv:2305.18997. [Google Scholar] [CrossRef]
- Brown, T.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J.D.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; et al. Language Models are Few-Shot Learners. In Advances in Neural Information Processing Systems; Curran Associates, Inc.: Red Hook, NY, USA, 2020; Volume 33, pp. 1877–1901. Available online: https://proceedings.neurips.cc/paper_files/paper/2020/hash/1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html (accessed on 26 August 2023).
- Amershi, S.; Weld, D.; Vorvoreanu, M.; Fourney, A.; Nushi, B.; Collisson, P.; Suh, J.; Iqbal, S.; Bennett, P.N.; Inkpen, K.; et al. Guidelines for Human-AI Interaction. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, Glasgow, UK, 4–9 May 2019; Association for Computing Machinery: New York, NY, USA, 2019; pp. 1–13. [Google Scholar] [CrossRef]
- Goel, R.; Vashisht, S.; Dhanda, A.; Susan, S. An Empathetic Conversational Agent with Attentional Mechanism. In Proceedings of the 2021 International Conference on Computer Communication and Informatics (ICCCI), Rhodes, Greece, 29 September–1 October 2021; pp. 1–4. [Google Scholar] [CrossRef]
- Kuo, C.-H.; Chen, C.-T.; Lin, S.-J.; Huang, S.-H. Improving Generalization in Reinforcement Learning–Based Trading by Using a Generative Adversarial Market Model. IEEE Access 2021, 9, 50738–50754. [Google Scholar] [CrossRef]
- Li, Y.; Pan, Q.; Wang, S.; Yang, T.; Cambria, E. A Generative Model for category text generation. Inf. Sci. 2018, 450, 301–315. [Google Scholar] [CrossRef]
- Zini, J.E.; Awad, M. On the Explainability of Natural Language Processing Deep Models. ACM Comput. Surv. 2022, 55, 1–31. [Google Scholar] [CrossRef]
- Bommasani, R.; Hudson, D.A.; Adeli, E.; Altman, R.; Arora, S.; von Arx, S.; Bernstein, M.S.; Bohg, J.; Bosselut, A.; Brunskill, E.; et al. On the Opportunities and Risks of Foundation Models. arXiv 2022, arXiv:2108.07258. [Google Scholar] [CrossRef]
- Saka, A.B.; Oyedele, L.O.; Akanbi, L.A.; Ganiyu, S.A.; Chan, D.W.M.; Bello, S.A. Conversational artificial intelligence in the AEC industry: A review of present status, challenges and opportunities. Adv. Eng. Inform. 2023, 55, 101869. [Google Scholar] [CrossRef]
- Patton, D.U.; Landau, A.Y.; Mathiyazhagan, S. ChatGPT for Social Work Science: Ethical Challenges and Opportunities. J. Soc. Soc. Work Res. 2023, 14, 3. [Google Scholar] [CrossRef]
- Piñeiro-Martín, A.; García-Mateo, C.; Docío-Fernández, L.; López-Pérez, M. del C. Ethical Challenges in the Development of Virtual Assistants Powered by Large Language Models. Electronics 2023, 12, 3170. [Google Scholar] [CrossRef]
- Liu, P.; Yuan, W.; Fu, J.; Jiang, Z.; Hayashi, H.; Neubig, G. Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing. ACM Comput. Surv. 2023, 55, 1–35. [Google Scholar] [CrossRef]
- Activity. Dr. Bradley Hyatt. LinkedIn. Available online: https://www.linkedin.com/in/bradleyhyatt/recent-activity/all/ (accessed on 18 September 2023).
- Cohn, C.; Hutchins, N.; Biswas, G. Towards a Formative Feedback Generation Agent: Leveraging a Human-in-the-Loop, Chain-of-Thought Prompting Approach with LLMs to Evaluate Formative Assessment Responses in K-12 Science. 2023. Available online: https://par.nsf.gov/biblio/10468997-towards-formative-feedback-generation-agent-leveraging-human-loop-chain-thought-prompting-approach-llms-evaluate-formative-assessment-responses-science (accessed on 10 December 2023).
- Dai, S.-C.; Xiong, A.; Ku, L.-W. LLM-in-the-loop: Leveraging Large Language Model for Thematic Analysis. arXiv 2023, arXiv:2310.15100. [Google Scholar] [CrossRef]
GenAI Model Type | Characteristics | Advantages | Disadvantages |
---|---|---|---|
Generative Adversarial Network (GAN) | Two neural networks, a generator, and a discriminator, compete with each other to generate realistic data. |
|
|
Variational AutoEncoder (VAE) | Encodes data into a latent space and then decodes it back into the original space. |
|
|
Autoregressive models | Generate data one step at a time, using the previously generated data as input. |
|
|
Diffusion models | Start with a noisy image and gradually refine it to a realistic image. |
|
|
Flow-based models | Transform data from one distribution to another using a series of invertible functions. |
|
|
LLM | Developed by | Training Parameter Size (Billion) | Release Year | Access | |
---|---|---|---|---|---|
1 | GPT-4 | OpenAI | 1000+ | 2023 | Closed |
2 | PaLM | Google AI | 540 | 2022 | Open |
3 | MT-NLG | Nvidia | 530 | 2021 | Closed |
4 | Llama 2 | Meta AI | 500 | 2023 | Open |
5 | Gopher | DeepMind | 280 | 2021 | Open |
6 | GPT-3.5 | OpenAI | 175 | 2022 | Closed |
7 | GPT-3 | Open AI | 175 | 2020 | Closed |
8 | OPT | Meta AI | 175 | 2022 | Open |
9 | LaMDA | Google AI | 137 | 2022 | Open |
10 | GPT-NeoX | Microsoft | 100 | 2023 | Closed |
Perspectives: Main Points | Key Theme |
---|---|
Applying GenAI for construction management of documents in different format and sources. | Construction Documents and Data Management |
Quick enterprise data search. | |
Data management and extraction ultimately offers time-saving benefits and increased productivity when effectively leveraged. | |
For example, Integrating GenAI in scheduling to identify the most effective schedule path to follow. | |
Can help improve conversations and collaboration between project stakeholders such as contractors, designers, and owners. | Question Answering (QnA): |
Stakeholder demands for faster, affordable, and sustainable builds create opportunities for GenAI and automation to address construction’s unique challenges such as repetitive tasks and unsafe work environments. | Automation for Unique Challenges |
AI-generated designs and plans reduce manual work, enhancing data systems for faster payments, fewer errors, and better decisions. | AI-Generated Designs |
Generative AI increases predictive capabilities, leveraging historical data for accurate project forecasting, forecasting of trends, risk assessment, and opportunity identification. | Accurate Forecasting |
Incorporating GenAI streamlines the synthesis of project data and provides avenues for automating intricate information management, such as contract-related data, thereby enhancing decision-making during the initial phases of construction. | Project Data Synthesis |
AI and modern innovations in construction address labor shortages, cost escalation, and environmental concerns, positioning the industry for a transformative future. | Efficiency and Sustainability |
Integrate materials assessment AI tools to support quick and informed materials selection for improved sustainability, maximizing de-carbonization. | Materials Assessment |
The development of GenAI, like ChatGPT, improves human capabilities rather than replacing jobs. | AI Augmentation |
Phase | Potential GenAI Application | Main Beneficiary | Model Type Based on the Output |
---|---|---|---|
Feasibility [142,143,144,145] |
| Stakeholders | text-to-text |
| Owner | text-to-text | |
| Owner | text-to-text | |
| Stakeholders | text-to-image | |
| Stakeholders | text-to-text | |
| Stakeholders | text-to-text | |
Design [142,143,144,145] |
| Architect | text-to-task |
| Stakeholders | text-to-3D | |
| Owner | text-to-text | |
| Contractor | text-to-text | |
| Engineer | text-to-text | |
| Engineers | text-to-text | |
| Architect | text-to-text | |
| Stakeholders | text-to-text | |
| Architect | text-to-text | |
| Architect | text-to-task | |
| Architect | text-to-task | |
| Owner | text-to-text | |
Procurement [142,143,144,145] |
| Logistics team | text-to-3D |
| Procurement team | text-to-text | |
| Project manager | text-to-text | |
| Contractor | text-to-text | |
| Procurement team | text-to-text | |
Construction [142,143,144,145] |
| Contractor | text-to-text |
| Contractor | text-to-text | |
| Contractor | text-to-text | |
| Contractor | text-to-text | |
| Contractor | text-to-text | |
| Estimator | text-to-task | |
| text-to-task | ||
| text-to-task | ||
| text-to-text | ||
| Contractor | text-to-text | |
| Contractor | text-to-task | |
| Safety manager | text-to-image/text-to-video | |
| Project manager | text-to-image/text-to-video | |
Operation and Maintenance [142,143,144,145] |
| Facility manager | text-to-text |
| Technician | text-to-text | |
| Technician | text-to-3D | |
| Facility manager | text-to-text/text-to-image | |
| Facility manager | text-to-text | |
| Occupants | text-to-text | |
Any Phase |
| Stakeholders | text-to-text/text-to-3D |
Any Phase |
| Stakeholders | text-to-task/text-to-3D |
Any Phase |
| Stakeholders | text-to-task |
Any Phase |
| Stakeholders | text-to-task |
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Ghimire, P.; Kim, K.; Acharya, M. Opportunities and Challenges of Generative AI in Construction Industry: Focusing on Adoption of Text-Based Models. Buildings 2024, 14, 220. https://doi.org/10.3390/buildings14010220
Ghimire P, Kim K, Acharya M. Opportunities and Challenges of Generative AI in Construction Industry: Focusing on Adoption of Text-Based Models. Buildings. 2024; 14(1):220. https://doi.org/10.3390/buildings14010220
Chicago/Turabian StyleGhimire, Prashnna, Kyungki Kim, and Manoj Acharya. 2024. "Opportunities and Challenges of Generative AI in Construction Industry: Focusing on Adoption of Text-Based Models" Buildings 14, no. 1: 220. https://doi.org/10.3390/buildings14010220