  Smart maintenance of historic buildings involves integration of digital
technologies and data analysis methods to help maintain functionalities of
these buildings and preserve their heritage values. However, the maintenance of
historic buildings is a long-term process. During the process, the digital
transformation requires overcoming various challenges, such as stable and
scalable storage and computing resources, a consistent format for organizing
and representing building data, and a flexible design to integrate data
analytics to deliver applications. This licentiate thesis aims to address these
challenges by proposing a digitalization framework that integrates Internet of
Things (IoT), cloud computing, ontology, and machine learning. IoT devices
enable data collection from historic buildings to reveal their latest status.
Using a public cloud platform brings stable and scalable resources for storing
data, performing analytics, and deploying applications. Ontologies provide a
clear and concise way to organize and represent building data, which makes it
easier to understand the relationships between different building components
and systems. Combined with IoT devices and ontologies, parametric digital twins
can be created to evolve with their physical counterparts. Furthermore, with
machine learning, digital twins can identify patterns from data and provide
decision-makers with insights to achieve smart maintenance. Overall, this
thesis contributes to the field of preservation of historic buildings by
proposing a comprehensive digitalization framework that integrates various
advanced digital technologies to provide a holistic approach to achieve smart
maintenance of historic buildings.
