Work

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

Year: 2025

Type: article

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.

Source: International Journal of Sustainable Energy

DOI: 10.1080/14786451.2025.2544238

Authors: Elli Kyriaki, Effrosyni Giama, Ifigeneia Theodoridou

Institution: Aristotle University of Thessaloniki

Language: English

Topic: BIM and Construction Integration

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: diamond

Funder: European Regional Development Fund