Journal
JOURNAL OF LOW FREQUENCY NOISE VIBRATION AND ACTIVE CONTROL
Volume 39, Issue 3, Pages 545-559Publisher
SAGE PUBLICATIONS LTD
DOI: 10.1177/1461348418786520
Keywords
Bayesian network; environmental factor; model class selection; structural health monitoring
Categories
Funding
- National Natural Science Foundation of China [51508201, 51678252]
- Natural Science Foundation of Guangdong Province, China [2017A030313262]
- Pearl River S&T Nova Program of Guangzhou [201806010172]
- Science and Technology Program of Guangzhou [201804020069]
Ask authors/readers for more resources
Modal frequency is an important indicator reflecting the health status of a structure. Numerous investigations have shown that its fluctuations are related to the changing environmental factors. Thus, modelling the modal frequency-multiple environmental factors relation is essential for making reliable inference in structural health monitoring. In this study, the Bayesian network (BN)-based algorithm is developed for recognizing the pattern between modal frequency and multiple environmental factors. Different candidates of network structure of the BN are proposed to describe the possible statistical relations of different variables. In the BN-based pattern recognition, the learning phase conducts uncertainty quantification in both parameter and model levels; and the prediction phase makes inference under complete and incomplete observed information. Based on the long-term monitoring data, the most plausible network structure is selected, and its associated parameters are identified. The developed algorithm is then utilized for analyzing the long-term monitoring data (modal frequencies, temperature, humidity, wind speed and traffic volume) of the Xinguang Bridge (a 782-m three-span half-through arch bridge). It turns out that the selected network structure properly captures the pattern of modal frequency-multiple environmental factors.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available