Buy article PDF
The purchased file will be sent to you
via email after the payment is completed.
US$ 35
|
Smart Structures and Systems Volume 24, Number 4, October 2019 , pages 507-524 DOI: https://doi.org/10.12989/sss.2019.24.4.507 |
|
|
|
Bayesian forecasting approach for structure response prediction and load effect separation of a revolving auditorium |
||
Zhi Ma, Chung-Bang Yun, Yan-Bin Shen, Feng Yu,
Hua-Ping Wan and Yao-Zhi Luo
|
||
| Abstract | ||
| A Bayesian dynamic linear model (BDLM) is presented for a data-driven analysis for response prediction and load effect separation of a revolving auditorium structure, where the main loads are self-weight and dead loads, temperature load, and audience load. Analyses are carried out based on the long-term monitoring data for static strains on several key members of the structure. Three improvements are introduced to the ordinary regression BDLM, which are a classificatory regression term to address the temporary audience load effect, improved inference for the variance of observation noise to be updated continuously, and component discount factors for effective load effect separation. The effects of those improvements are evaluated regarding the root mean square errors, standard deviations, and 95% confidence intervals of the predictions. Bayes factors are used for evaluating the probability distributions of the predictions, which are essential to structural condition assessments, such as outlier identification and reliability analysis. The performance of the present BDLM has been successfully verified based on the simulated data and the real data obtained from the structural health monitoring system installed on the revolving structure. | ||
| Key Words | ||
| Bayesian dynamic linear model; data-driven method; response prediction; load effect separation; revolving structure; structural health monitoring | ||
| Address | ||
| Zhi Ma, Chung-Bang Yun, Yan-Bin Shen, Feng Yu, Hua-Ping Wan and Yao-Zhi Luo: College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, Zhejiang, China : | ||