Optimisation of the sampling frequency of tilt sensors for building safety monitoring
This study presents a novel method for optimizing the sampling frequency of tilt sensors used in building safety monitoring. By integrating data thinning techniques and the ARIMA (Auto-Regressive Integrated Moving Average) model, the method aims to reduce data redundancy while retaining critical trends and features in monitoring data. The method includes data thinning, ARIMA model fitting for predictive analysis, and an accuracy evaluation process. Thinning factors are used to reduce the original data into different thinned datasets. We then divide each dataset into a training set and a test set, using the ARIMA model to fit the training set and create a prediction dataset. The accuracy evaluation combines qualitative and numerical assessments to thoroughly analyze and evaluate the deviation between the prediction and test datasets. The optimal sampling frequency is determined based on the thinned dataset with the best accuracy. Experimental validation is performed on data from the LuAnZhou border barracks. Results demonstrate that by adjusting the sensor's sampling interval from two hours to four hours, the method effectively optimizes sensor frequency without compromising data accuracy. This approach is crucial for ensuring long-term, cost-effective monitoring of large-scale buildings, offering a significant reduction in data acquisition and processing costs.
Funding
The National Natural Science Foundation of China (Grant No. 42271420)
The Natural Science Foundation of Zhejiang Province (Grant No. LGEZ25E090008)
The Natural Science Foundation of Jiangsu Province (Grant No. BK20220367)
The Science and Technology Plan Project of the Ministry of Housing and Urban-Rural Development of China in 2022 (Grant No. 2022-K-041)
History
School
- Architecture, Building and Civil Engineering
Published in
Survey ReviewPublisher
Informa UK Limited (Taylor & Francis Group)Version
- AM (Accepted Manuscript)
Rights holder
© Survey Review LtdPublisher statement
This is an Accepted Manuscript version of the following article, accepted for publication in Survey Review. It is deposited under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited.Acceptance date
2025-03-12Publication date
2025-03-20Copyright date
2025ISSN
0039-6265eISSN
1752-2706Publisher version
Language
- en