Bath Engineering & Design Studentships: People-Led Digital Twins for Preventive Conservation of Built Heritage Assets

This project is one of a number in competition for a studentship from the Faculty of Engineering and Design at the University of Bath. If successful, this studentship is expected to commence 04 October 2027.

An alternative start date may be possible if agreed with your intended supervisors and the Doctoral College.

Project Description

Historic buildings and structures are exposed to accelerating pressures from climate change, ageing materials, and constrained conservation budgets, yet decisions about how and when to intervene are still made with limited, fragmented, and often inaccessible information. Digital twins [1] (dynamic digital representations of physical assets, continuously updated with monitoring and inspection data) offer a route to better-informed conservation decisions. However, most digital twin research to date has focused on technical performance, with little attention paid to how the professionals and communities who actually manage, use, and care about heritage assets interact with these systems [2, 3].

This PhD addresses that gap by developing and testing a people-led approach [4] to digital twins for built heritage conservation. Rather than treating the digital twin as a fully automated decision-making tool, the project positions it as a decision-support environment that keeps conservation professionals, engineers, and site stakeholders actively "in the loop", interpreting monitoring data, weighing competing conservation priorities, and validating recommended interventions against professional judgement and heritage significance.

The research will combine structural health monitoring, sensor data, and inspection records with interactive visualisation and explainable AI methods, applied to real historic case studies. It will investigate how digital twin interfaces can be designed to support transparent, trustworthy human decision-making rather than replace it; how monitoring data can be translated into forms that are genuinely usable by non-specialist stakeholders; and how a human-in-the-loop workflow performs against purely automated alternatives in terms of accuracy, trust, and adoption.

Expected outcomes include a validated people-led digital twin framework, open-source tools for heritage monitoring and interpretation, and evidence-based guidance for heritage organisations on responsibly adopting digital twin technology. The work will be grounded in case studies developed through the candidate's supervisory team's existing partnerships with heritage-owning organisations, giving direct access to real assets, data, and practitioner feedback throughout the project.