Research on Maintenance and Management Strategies of Buildings based on Machine Learning

DOI

10.2991/978-94-6463-398-6_2How to use a DOI?

Keywords

Building Maintenance; Management Strategies; Equipment States; Machine Learning

Abstract

As architectural theory is advancing by leaps and bound, tremendous and complex buildings has constructed and requires corresponding maintenance and management methods. However, existing management concentrate on property management and the maintenance relies on human detection. Therefore, it is urgent to develop a complete set of reasonable and scientific management mechanisms and business models for the development of the existing construction equipment management industry. In this work, we apply a machine learning model and combine the big data management technologies including predictive maintenance models and data-driven strategies. The machine learning model can replace traditional detection method by learning complex sensors data from buildings. Our model can effectively predict building equipment failures, optimize energy use, and reduce maintenance costs. From our extensive experiments and analysis, we can observe that our proposed model can provide reasonable maintenance strategies for distinctive buildings and generate acceptable management methods.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Liangxiong Wang
AU  - Lifeng Gao
PY  - 2024
DA  - 2024/04/24
TI  - Research on Maintenance and Management Strategies of Buildings based on Machine Learning
BT  - Proceedings of the 2023 5th International Conference on Hydraulic, Civil and Construction Engineering (HCCE 2023)
PB  - Atlantis Press
SP  - 4
EP  - 10
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-398-6_2
DO  - 10.2991/978-94-6463-398-6_2
ID  - Wang2024
ER  -