Generative AI in the Construction Industry: Opportunities & Challenges

• URL: http://arxiv.org/abs/2310.04427v1
• Date: Tue, 19 Sep 2023 18:20:49 GMT
• Title: Generative AI in the Construction Industry: Opportunities & Challenges
• Authors: Prashnna Ghimire, Kyungki Kim, Manoj Acharya
• Abstract summary: Current surge lacks a study investigating the opportunities and challenges of implementing Generative AI (GenAI) in the construction sector. This study delves into reflected perception in literature, analyzes the industry perception using programming-based word cloud and frequency analysis. 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.
• Score: 2.562895371316868
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 (LLM) 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 & engineering domains.

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