  The construction industry is a vital sector of the global economy, but it
faces many productivity challenges in various processes, such as design,
planning, procurement, inspection, and maintenance. Generative artificial
intelligence (AI), which can create novel and realistic data or content, such
as text, image, video, or code, based on some input or prior knowledge, offers
innovative and disruptive solutions to address these challenges. However, there
is a gap in the literature on the current state, opportunities, and challenges
of generative AI in the construction industry. This study aims to fill this gap
by providing a state-of-the-art analysis of generative AI in construction, with
three objectives: (1) to review and categorize the existing and emerging
generative AI opportunities and challenges in the construction industry; (2) to
propose a framework for construction firms to build customized generative AI
solutions using their own data, comprising steps such as data collection,
dataset curation, training custom large language model (LLM), model evaluation,
and deployment; and (3) to demonstrate the framework via a case study of
developing a generative model for querying contract documents. The results show
that retrieval augmented generation (RAG) improves the baseline LLM by 5.2,
9.4, and 4.8% in terms of quality, relevance, and reproducibility. This study
provides academics and construction professionals with a comprehensive analysis
and practical framework to guide the adoption of generative AI techniques to
enhance productivity, quality, safety, and sustainability across the
construction industry.
