Year: 2025
Type: conference-paper
Abstract: The construction industry is beginning to explore generative artificial intelligence (AI) technologies to address complex data management challenges and inefficiencies within traditional workflows, particularly in design, planning, and project management. Although there is growing interest in generative AI in construction, the understanding of its trends and opportunities remains scattered. This paper investigates the current trends, applications, and research needs for generative AI in construction through a bibliometric analysis of 148 publications from Scopus and Google Scholar. The keyword co-occurrence map highlights several search clusters, with a strong focus on "Building Information Modeling (BIM)," "digital twins," "sustainability," "construction safety," "structural and architectural design," "education," and "project management." Generative AI technologies like Generative Adversarial Networks (GANs), large language models (LLMs), Generative Pre-trained Transformer (GPT), and diffusion models play critical roles in this research area. By pinpointing key research gaps, it underscores avenues for future investigation and empowers researchers to expand and refine the latest advancements in this dynamic field, propelling progress toward a more efficient, resilient, and sustainable built environment.
Source: CIB Conferences
Authors: Zaid Alwashah, hexu liu, Bo Xiao, Shane Mueller, Xiaoyun Shao
Language: English
Subfield: Building and Construction
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Funder: National Science Foundation
OpenAlex ID: W4411473204