Trend-Analysis and Research Direction in Construction Management Literature

Larsen, Jesper Kranker; Ussing, Lene Faber; Brunø, Thomas Ditlev

doi:10.1061/9780784413135.007

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…The findings highlighted trending topics such as project performance indicators; information and communication technologies including Building Information Modeling (BIM); and quantitative methods for CEM. Moreover, some researchers focused on analyzing publication metrics and trends in some specific CEM subdisciplines, such as construction labor productivity [19]; planning and scheduling [20]; building information modeling (BIM) [16]; and artificial intelligence adoption [21], among others. In addition, some studies focused on studying the quality of publications as well as impactful factors.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Future Research Trends in the Construction Engineering and Management Domain Using Machine Learning and Social Network Analysis

Ali,

El-adaway,

Ahmed

et al. 2024

Modelling

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Construction Engineering and Management (CEM) is a broad domain with publications covering interrelated subdisciplines and considered a key source of knowledge sharing. Previous studies used scientometric methods to assess the current impact of CEM publications; however, there is a need to predict future citations of CEM publications to identify the expected high-impact trends in the future and guide new research efforts. To tackle this gap in the literature, the authors conducted a study using Machine Learning (ML) algorithms and Social Network Analysis (SNA) to predict CEM-related citation metrics. Using a dataset of 93,868 publications, the authors trained and tested two machine learning classification algorithms: Random Forest and XGBoost. Validation of the RF and XGBoost resulted in a balanced accuracy of 79.1% and 79.5%, respectively. Accordingly, XGBoost was selected. Testing of the XGBoost model revealed a balanced accuracy of 80.71%. Using SNA, it was found that while the top CEM subdisciplines in terms of the number of predicted impactful papers are “Project planning and design”, “Organizational issues”, and “Information technologies, robotics, and automation”; the lowest was “Legal and contractual issues”. This paper contributes to the body of knowledge by studying the citation level, strength, and interconnectivity between CEM subdisciplines as well as identifying areas more likely to result in highly cited publications.

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