2026-10-14T03:52:56Z https://data.mendeley.com/oai

oai:data.mendeley.com/p2wfnw2tzc.1 2026-06-13T01:31:27Z

Abiola-Ogedengbe, Diekolola Artificial Intelligence and Digitalisation in the Construction Industry: A Scientometric Analysis of Structural Integration, Thematic Acceleration and the Sustainability Integration Gap Mendeley Data Purpose – This study maps the intellectual structure of research on artificial intelligence (AI) and digitalisation in the construction industry, moving beyond descriptive science mapping to a network-analytic diagnosis of how the field is structurally integrated, how rapidly its themes are accelerating, and whether its sustainability dimension is structurally embedded in the field. Design/methodology/approach – A corpus of 613 Scopus-indexed documents (2016–2026) was analysed through bibliometric performance analysis, keyword co-occurrence mapping in VOSviewer, and a reconstructed co-occurrence network subjected to multi-measure centrality analysis, cluster-cohesion estimation, modularity computation, and temporal emergence detection. Findings – The field has grown at a compound annual rate of 39.6 per cent to a corpus h-index of 73. Betweenness-centrality analysis reveals a hub-and-spoke architecture in which construction industry is the singular structural bridge (betweenness 0.98) despite high network density (0.77). Cluster-cohesion analysis uncovers a descending integration gradient across the field's four thematic clusters, from a well-integrated digital-BIM core (cohesion 0.561) to a barely-integrated sustainability cluster (0.106). Emergence analysis shows that machine-learning and deep-learning methods are accelerating most rapidly, while sustainability themes are both the least cohesive and among the slower-accelerating. The study terms this divergence the sustainability integration gap. Originality/value – This is the first scientometric study of AI and digitalisation in construction to combine multi-measure centrality, cluster-cohesion estimation, and emergence detection, and the first to demonstrate empirically that the field is deepening technologically while leaving its sustainability dimension structurally detached. Artificial Intelligence Construction Sustainability Digital Technology Dataset doi:10.17632/p2wfnw2tzc.1 oai:data.mendeley.com/p2wfnw2tzc.1 Creative Commons Attribution 4.0 International http://creativecommons.org/licenses/by/4.0 https://data.mendeley.com/datasets/p2wfnw2tzc 2026-06-13T01:31:27Z