Digital twins for dynamic life cycle assessment in the built environment
Ioan Petri, Amin Amin, Ali Ghoroghi, Andrei Hodorog, Yacine Rezgui
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.