A Digitalization Framework for Smart Maintenance of Historic Buildings
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Cloud ComputingSimilar Papers 제목 키워드 기반
Automatic Reconstruction of Semantic 3D Models from 2D Floor Plans
Digitalization of existing buildings and the creation of 3D BIM models for them has become crucial for many tasks. Of particular importance are floor plans, which contain information about building layouts and are vital …
Parametric Digital Twins for Preserving Historic Buildings: A Case Study at Löfstad Castle in Östergötland, Sweden
This study showcases the digitalization of L\"ofstad Castle in Sweden to contribute to preserving its heritage values. The castle and its collections are deteriorating due to an inappropriate indoor climate. To address t…
A Semantic-driven Approach for Maintenance Digitalization in the Pharmaceutical Industry
The digital transformation of pharmaceutical industry is a challenging task due to the high complexity of involved elements and the strict regulatory compliance. Maintenance activities in the pharmaceutical industry play…
Intelligent Energy Management with IoT Framework in Smart Cities Using Intelligent Analysis: An Application of Machine Learning Methods for Complex Networks and Systems
This study confronts the growing challenges of energy consumption and the depletion of energy resources, particularly in the context of smart buildings. As the demand for energy increases alongside the necessity for effi…
energy managementManagementPhysics-Guided Memory Network for Building Energy Modeling
Accurate energy consumption forecasting is essential for efficient resource management and sustainability in the building sector. Deep learning models are highly successful but struggle with limited historical data and b…