A Digitalization Framework for Smart Maintenance of Historic Buildings
Zhongjun Ni (Department of Science, Technology, Link\"oping, University, Campus Norrk\"oping, Norrk\"oping, Sweden)

TL;DR
This paper proposes a comprehensive digitalization framework combining IoT, cloud computing, ontologies, and machine learning to enable smart maintenance and preservation of historic buildings.
Contribution
It introduces an integrated digitalization framework that addresses data organization, scalability, and analytics for historic building maintenance, advancing preservation technology.
Findings
IoT enables real-time data collection from historic buildings.
Cloud platforms provide scalable storage and analytics resources.
Ontologies facilitate understanding of building data relationships.
Abstract
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…
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