The purpose of this research is to look backward the status quo of big data research in the construction industry in the last 15 years, from 2010 to 2024 and look forward the research frontiers and openings for future research. In recent years, big data has increasingly become a new research hot spot in the construction industry and has rapidly developed into a new research branch of project management. However, few works were done to map the global study in this field, easily causing a neglect in new technological trends in the construction industry.
This research conducts a holistic literature review mainly based on bibliometrics approaches. A total of 2,986 bibliographic records from the Web of Science core collection database were collected and the tool of CitespaceV, Statistical Analysis Toolkit for Informatics 3.2 and Python 3.9.0 were adopted for this research.
This research finds that the evolution path can be categorized into three phrase, consisting of the conceptualization stage (2010–2014), the initial development stage (2015–2021) and the diffusion development stage (2022–present). Internet of Things, machine learning, cloud computing, artificial intelligence, deep learning, Kansei engineering and large language models (LLMs) are hot spots in this research field. Artificial intelligence, transfer learning and safety are the research frontier. Further research on unified data ontology and quality assurance frameworks, cost-effective analytics platforms for SMEs, next-generation workforce training ecosystems and emerging technology applications of big data in the construction industry shall be taken seriously.
This study advances big data research in the construction industry by extending temporal coverage (2010–2024), introducing multidimensional evolutionary analysis with hot spot/frontier differentiation and proposing actionable implementation pathways beyond extant literature.
