Digital Twins and Artificial Intelligence for the Environmental Performance, Management and Conservation of Modern Architectural and Urban Heritage: Case Studies in Brazilian University Campuses

This project develops guidelines for creating Digital Twins enriched with Artificial Intelligence to support the management, maintenance, conservation, and environmental performance of modern Brazilian architectural and urban heritage. The research integrates 3D digital surveying (laser scanning and point‑cloud processing), parametric modelling (HBIM), and IoT‑based environmental sensing. A multidisciplinary Brazil–UK consortium, comprising researchers in architecture, urbanism, engineering, and computer science, conducts case studies of modern buildings on university campuses. Expected outcomes include protocols for Digital Twin development, AI‑based analysis methods, capacity building, and the establishment of an international research network on digital heritage management.

Brazil has an important collection of modernist university buildings that now require careful conservation, technical updating, and improved environmental performance. This project uses digital technologies, such as 3D scanning, building information modelling, sensors, and artificial intelligence, to create “digital twins” of selected buildings. These virtual models allow the research team to understand how the buildings behave, identify problems, and support better maintenance and preservation decisions. The project brings together experts from several Brazilian universities and the University of Bath, helping to build national capacity in the use of advanced digital tools for heritage conservation.

Digital Twins that support the management, conservation and environmental optimisation of modern architectural and urban heritage. It will demonstrate how Artificial Intelligence can enhance the interpretation of environmental and operational data, enabling predictive maintenance and improved decision‑making in heritage settings. Through detailed case studies, the project will deliver high‑fidelity digital models that showcase integrated workflows combining 3D surveying, HBIM modelling and IoT‑based sensing. It is anticipated that the work will reveal both the potential and the limitations of AI‑driven approaches for built‑heritage management, offering insights into issues of automation, data quality and applicability. The project will further strengthen national and international research networks while building specialist capacity among postgraduate researchers in advanced surveying, digital modelling and AI. Overall, the findings will provide a foundational evidence base for using Digital Twins and AI to support sustainable, data‑informed preservation of modern architectural heritage across diverse campus environments.