Abstract
Heritage buildings hold cultural, historical, and architectural significance. For heritage
buildings to continue to benefit people, communities, and countries, they should be conserved.
Since conservation and maintenance both prolong lifespan, maintenance is required to
conserve a heritage building. Maintenance management processes are important for heritage
buildings to survive in the world today. Without maintenance, these heritage buildings face
outcomes that do not allow for a viable end. If these heritage buildings become extinct, the
world will face a loss of cultural value, it will affect the economy negatively as tourist attractions
fall into demise, and communities will not be able to use the facilities of these heritage
buildings. With the rapid advancements of the Fourth Industrial Revolution, there is an
increasing need for heritage buildings to integrate modern technologies, such as digital twins,
to enhance their maintenance management processes.
This study addresses the question: How can a framework for a digital twin of maintenance
management processes for heritage buildings be developed? A comprehensive literature
review informed the development of the framework, identifying essential maintenance
requirements, digital twin applications across various building types and maintenance
management processes, and specific data elements for condition assessments. Similarities
across these outcomes informed the development of a framework, which includes input,
process, and output data for effective maintenance management in heritage buildings
Based on the above process, a simple feedforward neural network was chosen for its
suitability in managing and processing the maintenance data elements in the digital twin
framework. This study validates that the digital twin technology can be used for maintenance
management processes in heritage buildings.