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

Saka, Abdullahi; Taiwo, Ridwan; Saka, Nurudeen; Salami, Babatunde Abiodun; Ajayi, Saheed; Akande, Kabiru; Kazemi, Hadi

doi:10.1016/j.dibe.2023.100300

Cited by 17 publications

(6 citation statements)

References 68 publications

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“…Therefore, targeted training programs are crucial to developing wellrounded internal capabilities. Finally, the vulnerability of connected tools or data handling processes to malicious threats leaves unprepared adopters exposed to crippling breaches as studied across industries [123,124]. Also, lack of transparency around data rights or algorithmic decision-making processes also introduces major ethical risks.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence in the Construction Industry: A Systematic Review of the Entire Construction Value Chain Lifecycle

Egwim,

Alaka,

Demir

et al. 2023

Energies

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In recent years, there has been a surge in the global digitization of corporate processes and concepts such as digital technology development which is growing at such a quick pace that the construction industry is struggling to catch up with latest developments. A formidable digital technology, artificial intelligence (AI), is recognized as an essential element within the paradigm of digital transformation, having been widely adopted across different industries. Also, AI is anticipated to open a slew of new possibilities for how construction projects are designed and built. To obtain a better knowledge of the trend and trajectory of research concerning AI technology application in the construction industry, this research presents an exhaustive systematic review of seventy articles toward AI applicability to the entire lifecycle of the construction value chain identified via the guidelines outlined by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The review’s findings show foremostly that AI technologies are mostly used in facility management, creating a huge opportunity for the industry to profit by allowing facility managers to take proactive action. Secondly, it shows the potential for design expansion as a key benefit according to most of the selected literature. Finally, it found data augmentation as one of the quickest prospects for technical improvement. This knowledge will assist construction companies across the world in recognizing the efficiency and productivity advantages that AI technologies can provide while helping them make smarter technology investment decisions.

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Section: Discussionmentioning

confidence: 99%

Artificial Intelligence in the Construction Industry: A Systematic Review of the Entire Construction Value Chain Lifecycle

Egwim,

Alaka,

Demir

et al. 2023

Energies

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show abstract

“…However, promising avenues exist to address these knowledge gaps. For instance, large language models like GPT require fine-tuning and contextual input tailored to the construction domain in order to efficiently generate industry-specific insights [149]. Hybrid reasoning techniques combining top-down ontological, symbolic knowledge with bottom-up neural networks can be beneficial.…”

Section: Domain Knowledgementioning

confidence: 99%

“…Successful implementation requires AI understanding, skillsets, and trainings so that industry experts can properly utilize these models. One of the major skills required is proficiency in "prompt engineering", optimizing prompts to maximize model efficacy [149,160]. However, overreliance on automation risks in reduction in human expertise and the potential for errors in cases of AI malfunction or erroneous information provision [74].…”

Section: Construction Regulatory Challengesmentioning

confidence: 99%

Opportunities and Challenges of Generative AI in Construction Industry: Focusing on Adoption of Text-Based Models

Ghimire,

Kim,

Acharya

2024

Buildings

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In the last decade, despite rapid advancements in artificial intelligence (AI) transforming many industry practices, construction largely lags in adoption. Recently, the emergence and rapid adoption of advanced large language models (LLMs) like OpenAI’s GPT, Google’s PaLM, and Meta’s Llama have shown great potential and sparked considerable global interest. However, the current surge lacks a study investigating the opportunities and challenges of implementing Generative AI (GenAI) in the construction sector, creating a critical knowledge gap for researchers and practitioners. This underlines the necessity to explore the prospects and complexities of GenAI integration. Bridging this gap is fundamental to optimizing GenAI’s early stage adoption within the construction sector. Given GenAI’s unprecedented capabilities to generate human-like content based on learning from existing content, we reflect on two guiding questions: What will the future bring for GenAI in the construction industry? What are the potential opportunities and challenges in implementing GenAI in the construction industry? This study delves into reflected perception in literature, analyzes the industry perception using programming-based word cloud and frequency analysis, and integrates authors’ opinions to answer these questions. This paper recommends a conceptual GenAI implementation framework, provides practical recommendations, summarizes future research questions, and builds foundational literature to foster subsequent research expansion in GenAI within the construction and its allied architecture and engineering domains.

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“…Fine-tuned language models could revolutionize safety practices and predict construction accidents from unstructured free text data. However, their application in the construction industry is limited, necessitating further research and validation of use cases [28,31].…”

Section: Limited Exploration Of Generative Aimentioning

confidence: 99%

“…capabilities. They enhance demolition risk assessments, capture tacit knowledge, and provide multilingual support for knowledge management and training in construction [31]. Integration into site safety management opens opportunities for safety practices improvement, automated risk assessments, and real-time insights into hazards [31].…”

Section: Limited Exploration Of Generative Aimentioning

confidence: 99%

Highway Construction Safety Analysis Using Large Language Models

Smetana,

Salles de Salles,

Sukharev

et al. 2024

Applied Sciences

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The highway construction industry carries substantial safety risks for workers, necessitating thorough accident analyses to implement effective preventive measures. Current research lacks comprehensive investigations into safety incidents, relying heavily on conventional statistical methods and overlooking valuable textual information in publicly available databases. This study leverages a state-of-the-art large language model (LLM), specifically OpenAI’s GPT-3.5 model. The primary focus is to enhance text-based incident analysis that is sourced from OSHA’s Severe Injury Reports (SIR) database. By incorporating novel natural language processing (NLP) techniques, dimensionality reduction, clustering algorithms, and LLM prompting of incident narratives, the study aims to develop an approach to the analysis of major accident causes in highway construction. The resulting cluster analysis, coupled with LLM summarization and cause identification, reveals the major accident types, such as heat-related and struck-by injuries, as well as commonalities between incidents. This research showcases the potential of artificial intelligence (AI) and LLM technology in data-driven analysis. By efficiently processing textual data and providing insightful analysis, the study fosters practical implications for safety professionals and the development of more effective accident prevention and intervention strategies within the industry.

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

Cited by 17 publications

References 68 publications

Artificial Intelligence in the Construction Industry: A Systematic Review of the Entire Construction Value Chain Lifecycle

Artificial Intelligence in the Construction Industry: A Systematic Review of the Entire Construction Value Chain Lifecycle

Opportunities and Challenges of Generative AI in Construction Industry: Focusing on Adoption of Text-Based Models

Highway Construction Safety Analysis Using Large Language Models

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