Traditional data analysis methods are plagued by problems such as the difficulty of extracting features from the data and the low accuracy of removing noise from bridge monitoring data. Noise reduction analysis is implemented on the experimental sample monitoring data by using the topology data analysis method.
The time-delayed embedding simple complex form and noise reduction methods for topology data analysis are stated. A topology data analysis experiment is designed using the displacement sample monitoring data of an in-service assembled simple girder bridge as the research object, and noise reduction analysis is implemented on the experimental sample monitoring data by using the topology data analysis method.
The time-delayed embedding simple complex shape and noise reduction algorithms of topological data analysis are more innovative to be used for the analysis of one-dimensional time-series data. The experimental design is more reasonable and repeatable, and the experimental results show that the topological data analysis method can be used to reduce the noise of bridge structure monitoring data. This study contributes to the scientific analysis and evaluation of the performance of in-service bridges by improving the accuracy of the analysis of bridge monitoring data.
In this paper, topological data analysis methods are employed to study the implied topological features in bridge monitoring data and implement improvements to further enhance the noise reduction. This study contributes to the scientific analysis and evaluation of the performance of in-service bridges by improving the accuracy of the analysis of bridge monitoring data.
