Machine learning, artificial intelligence and digital twins: an up-to-date review analysis of the latest- era technologies in the urban building sector

Machine learning, Artificial Intelligence and Digital Twins are a dynamic and rapidly evolving field with important implications for various sectors. They enable the development of intelligent systems capable of performing complex tasks with high accuracy and efficiency. Against this background, this paper presents an up-to-date state-of-the-art of these technologies integrated into the building sector. More specifically, scientific papers only from the time period 2024–2025, were analysed in an effort to present the most recent research in this field. Overall, 117 papers were analysed, of which 26% concerned Digital Twins, 31% Artificial Intelligence and 43% Machine Learning, all in building applications. The analysis showed that various scientific journals deal with these topics, with the most popular amongst them being the Energy and Buildings journal. Lastly, based on the results of the bibliometric analysis and the affiliation data provided, 57% of the authors come from Asia and 23% from Europe.

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Publication Details

Journal
International Journal of Sustainable Energy
Published
2025-08-29
DOI
https://doi.org/10.1080/14786451.2025.2544238
Citations
4
Primary Topic
BIM and Construction Integration
Type
article
Field-Weighted Citation Impact
4.03

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article

Machine learning, artificial intelligence and digital twins: an up-to-date review analysis of the latest- era technologies in the urban building sector

Elli Kyriaki, Ifigeneia Theodoridou, Effrosyni Giama
4 citations
International Journal of Sustainable Energy
BIM and Construction Integration
4.03
article

Machine learning, artificial intelligence and digital twins: an up-to-date review analysis of the latest- era technologies in the urban building sector

Elli Kyriaki, Ifigeneia Theodoridou, Effrosyni Giama
article en
4 citations

Abstract

Machine learning, Artificial Intelligence and Digital Twins are a dynamic and rapidly evolving field with important implications for various sectors. They enable the development of intelligent systems capable of performing complex tasks with high accuracy and efficiency. Against this background, this paper presents an up-to-date state-of-the-art of these technologies integrated into the building sector. More specifically, scientific papers only from the time period 2024–2025, were analysed in an effort to present the most recent research in this field. Overall, 117 papers were analysed, of which 26% concerned Digital Twins, 31% Artificial Intelligence and 43% Machine Learning, all in building applications. The analysis showed that various scientific journals deal with these topics, with the most popular amongst them being the Energy and Buildings journal. Lastly, based on the results of the bibliometric analysis and the affiliation data provided, 57% of the authors come from Asia and 23% from Europe.

International Journal of Sustainable EnergyVol. 44(1)
Aristotle University of Thessaloniki (GR), E2A Architects (Switzerland) (CH)
European Regional Development Fund
Sustainable cities and communities
Openalex Percentile: Top 6%
BIM and Construction Integration
4.03
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