[Paper Review] Generative AI in the Construction Industry: Opportunities & Challenges

Prashnna Ghimire, Kyungki Kim+1 more|arXiv (Cornell University)|Sep 19, 2023

BIM and Construction IntegrationEngineering122 references3 citations

TL;DR

This paper investigates the opportunities and challenges of integrating generative AI (GenAI) in the construction industry, leveraging literature review, programming-based word cloud analysis, and expert insights to propose a conceptual GenAI implementation framework. It identifies key applications in design, documentation, and project management while highlighting risks related to data quality, bias, and ethical use, offering foundational guidance for future research and practical adoption.

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.

Motivation & Objective

• To address the critical knowledge gap in understanding how generative AI can be adopted in the construction industry.
• To analyze industry perceptions of GenAI through text mining techniques like word cloud and frequency analysis.
• To identify key opportunities and challenges in implementing GenAI across construction workflows.
• To propose a conceptual framework for early-stage GenAI integration in construction and allied AEC domains.
• To provide actionable recommendations and outline future research directions for GenAI in construction.

Proposed method

• Conduct a comprehensive literature review to map existing knowledge on GenAI in construction.
• Perform programming-based word cloud and frequency analysis on industry-related text data to extract perception trends.
• Integrate authors' expert opinions to interpret findings and validate insights.
• Synthesize results into a conceptual GenAI implementation framework tailored for construction and AEC sectors.
• Use qualitative synthesis to derive practical recommendations and future research questions.
• Apply a multidisciplinary lens combining AI, construction practices, and domain-specific challenges.

Experimental results

Research questions

• RQ1 What are the potential opportunities for generative AI in transforming construction industry practices?
• RQ2 What are the key challenges to implementing generative AI in construction, including technical, ethical, and operational barriers?
• RQ3 How do current industry perceptions align with the capabilities and limitations of generative AI models?
• RQ4 What conceptual framework can guide the responsible and effective implementation of GenAI in construction?
• RQ5 What future research directions are needed to advance GenAI adoption in architecture, engineering, and construction?

Key findings

• Generative AI shows strong potential to enhance design creativity, automate documentation, and improve project planning in construction.
• Despite high interest, the construction industry lags in AI adoption due to fragmented data, legacy systems, and resistance to change.
• Word cloud and frequency analysis revealed recurring themes such as 'efficiency,' 'automation,' 'design,' and 'data quality' in industry discourse.
• Key challenges include model hallucination, data privacy, bias in training data, and lack of domain-specific fine-tuning for construction contexts.
• The proposed conceptual framework outlines stages for GenAI integration, from data preparation to deployment and monitoring.
• The study establishes foundational literature to support future research on GenAI in AEC, emphasizing ethical and practical implementation.