Data · dataset · 2026
Artificial Intelligence and Digitalisation in the Construction Industry: A Scientometric Analysis of Structural Integration, Thematic Acceleration and the Sustainability Integration Gap
Listed in Teesside University Research Data Repository
Description
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).
Read the rest (1 more)
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.
Links
Where it is published
- DOI doi.org/10.17632/p2wfnw2tzc.1 ↗
DOI / persistent id · from researchdata tees ac uk
Catalogue records · 1
- OAI-PMH record data.mendeley.com/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Adata… ↗
metadata API · from researchdata tees ac uk
Topics
- From keywords
- Artificial intelligence · Computer Science & AI · Earth & Environmental Science · Engineering · Humanities · Life Sciences · Social Science
Provenance · 1 source records, 12 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Teesside University Research Data Repository | oai:data.mendeley.com/p2wfnw2tzc.1 | 11 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].anzsrc:group:4602 | mapping · researchdata tees ac uk | vocabulary-mapper@1.0.0 | keywords['Artificial Intelligence'] |
| concepts[field].local:field:computer-science-ai | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:engineering | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:humanities | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:social-science | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| description | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/description |
| license | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/rights |
| publication_date | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| title | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/title |