Scientometric review of articles published in ASCE's journal of construction engineering and management from 2000 to 2018

Ruoyu Jin, Jian Zuo, Jingke Hong

    Research output: Contribution to journalArticlepeer-review


    This study aims to address research questions related to the evolution of academic research in the field of construction engineering and management (CEM): (1) what are the mainstream research topics since 2000? (2) what are the emerging topics or techniques in CEM within the recent decades? and (3) what are potential CEM research areas in the near future? A scientometric analysis was conducted to review articles published in Journal of Construction Engineering and Management since 2000, follow by a qualitative discussion. This study revealed that project performance indicator-related topics (e.g., cost, scheduling, safety, productivity, and risk management) have been the ongoing mainstream issues over the last decades. Labor and personnel issues gained even more research attention in the last 10 years. Information and communication technologies [e.g., building information modeling (BIM)] applied in CEM has been gaining the momentum since 2009. A variety of quantitative methods have gained popularity in the CEM discipline, such as algorithms, statistics, fuzzy sets, and neural networks. The follow-up qualitative analysis led to the contributions of this review-based study in terms that (1) it provided an overview of the research topics in CEM since 2000 through a text-mining approach; and (2) it offered insights into emerging and near-future research areas, including BIM and data analytics applied in various construction issues (e.g., safety), as well as integrations of research themes (e.g., risk assessment in newly emerging project delivery methods).

    Original languageEnglish
    Article number06019001
    JournalJournal of Construction Engineering and Management
    Issue number8
    Publication statusPublished - 13 Jun 2019

    Bibliographical note

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    • Literature review
    • scientometric analysis
    • construction engineering and 29 management
    • text-mining
    • Text mining
    • Scientometric analysis
    • Construction engineering and management


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