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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.03685 |
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| _version_ | 1866911137152368640 |
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| author | Ni, Zhongjun |
| author_facet | Ni, Zhongjun |
| contents | Digital transformation in the built environment offers new opportunities to improve building maintenance through data-driven approaches. Smart monitoring, predictive modeling, and artificial intelligence can enhance decision-making and enable proactive strategies. The preservation of historic buildings is an important scenario where preventive maintenance is essential to ensure long-term sustainability while protecting heritage values. This thesis presents a comprehensive solution for data-driven smart maintenance of historic buildings, integrating Internet of Things (IoT), cloud computing, edge computing, ontology-based data modeling, and machine learning to improve indoor climate management, energy efficiency, and conservation practices.
This thesis advances data-driven conservation of historic buildings by combining smart monitoring, digital twins, and artificial intelligence. The proposed methods enable preventive maintenance and pave the way for the next generation of heritage conservation strategies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_03685 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data-Driven Smart Maintenance of Historic Buildings Ni, Zhongjun Systems and Control Digital transformation in the built environment offers new opportunities to improve building maintenance through data-driven approaches. Smart monitoring, predictive modeling, and artificial intelligence can enhance decision-making and enable proactive strategies. The preservation of historic buildings is an important scenario where preventive maintenance is essential to ensure long-term sustainability while protecting heritage values. This thesis presents a comprehensive solution for data-driven smart maintenance of historic buildings, integrating Internet of Things (IoT), cloud computing, edge computing, ontology-based data modeling, and machine learning to improve indoor climate management, energy efficiency, and conservation practices. This thesis advances data-driven conservation of historic buildings by combining smart monitoring, digital twins, and artificial intelligence. The proposed methods enable preventive maintenance and pave the way for the next generation of heritage conservation strategies. |
| title | Data-Driven Smart Maintenance of Historic Buildings |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2509.03685 |