Digital twins for dynamic life cycle assessment in the built environment

Petri, Ioan ORCID: https://orcid.org/0000-0002-1625-8247, Amin, Amin ORCID: https://orcid.org/0000-0002-6891-5640, Ghoroghi, Ali, Hodorog, Andrei ORCID: https://orcid.org/0000-0002-4701-5643 and Rezgui, Yacine ORCID: https://orcid.org/0000-0002-5711-8400 2025. Digital twins for dynamic life cycle assessment in the built environment. Science of the Total Environment 993 , 179930. 10.1016/j.scitotenv.2025.179930

PDF - Published Version Available under License Creative Commons Attribution. Download (2MB)

Abstract

Dynamic life cycle assessment (LCA) integrated with digital twin technologies is emerging as a transformative approach to evaluating and managing environmental performance in the built environment. This study presents the Building Life-cycle Digital Twin (BLDT) framework—a novel methodology that combines real-time data from Internet of Things (IoT) devices, machine learning algorithms, and semantic interoperability to deliver dynamic, predictive, and high-resolution LCA for construction and infrastructure systems. The framework, developed within the Computational Urban Sustainability Platform (CUSP), addresses the limitations of traditional static LCA by enabling continuous, data-driven sustainability assessments. Incorporating predictive modelling, BLDT empowers stakeholders with timely insights into energy use, emissions, and health and safety performance, supporting proactive environmental decision-making. Validated through a case study at the Port of Grimsby, the BLDT framework facilitated a 25% reduction in energy consumption while enhancing operational efficiency. These results demonstrate the model’s potential to support decarbonisation strategies, regulatory compliance, and long-term planning in the construction sector. By operationalising dynamic LCA through digital twins, this research contributes to the advancement of real-time sustainability analytics and resilient urban development.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Engineering
Publisher:	Elsevier
ISSN:	0048-9697
Date of First Compliant Deposit:	8 July 2025
Date of Acceptance:	14 June 2025
Last Modified:	08 Jul 2025 10:45
URI:	https://orca.cardiff.ac.uk/id/eprint/179588

Actions (repository staff only)

Edit Item