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    STATISTICAL MODELING OF THE RELATIONSHIP BETWEEN CITESCORE AND JOURNAL PERCENTILES OF BUILDING AND CONSTRUCTION JOURNALS
    (Pushpa Publishing House, 2023) Peter, N. J.; Iroham, Chukwuemeka O.
    Building and construction are one of the major subject categories in Scopus, Elsevier. Building and construction journals are journals that publish articles and review papers in all areas of building, construction, structures, construction materials, civil engineering, and building and construction management amongst others. This paper proposes a modified quartile model which can be used to predict the journal percentile using the CiteScore as the independent variable. The model evaluation metrics signal a good fit and the proposed model yielded journal percentile (JP) close to the original JP. The knowledge of the CiteScore can now be used to predict the percentile and by extension, the quartile of journals in building and construction in Scopus or Web of Science.
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    A Bibliometric Analysis of AI Trends in the AEC Industry
    (Preprints, 2025-09) Adewale, B. A.; Ene, Vincent Onyedikachi; Aigbavboa, Clinton Ohis
    This study employs a comprehensive bibliometric analysis to examine the evolving landscape of Artificial Intelligence (AI) research within the Architecture, Engineering, and Construction (AEC) industry over the past decade. Through systematic analysis of 68 publications from the Scopus database, utilizing co-authorship networks, citation analysis, and keyword co-occurrence mapping, the research reveals significant patterns and trends in AI adoption and research focus. The findings indicate a rapid growth in research output, with China, the United States, and the United Kingdom emerging as leading contributors. The analysis identifies four primary research clusters: AI integration across AEC processes, building lifecycle applications, digital technologies convergence, and automation techniques. A temporal evolution is observed, transitioning from basic automation to sophisticated applications involving machine learning, digital twins, and deep learning. The study highlights geographical disparities in research contributions and emphasizes the need for standardization in AI implementation. By providing insights into research trends, collaborative networks, and evolving focus areas, this analysis contributes to a deeper understanding of AI's role in transforming the AEC industry. The findings can guide future research directions, inform industry practitioners about emerging technologies, and support policymakers in developing frameworks for AI adoption in construction, ultimately facilitating more effective and responsible integration of AI technologies in AEC practices.