Predicting displacement of bridge based on CEEMDAN-KELM model using GNSS monitoring data
| Autor: | Yilin Xie, Dinh Tung Nguyen, Jiayong Yu, Xiaolin Meng, Qian Fan |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
020209 energy 02 engineering and technology computer.software_genre Displacement (vector) Bridge (nautical) GNSS applications Modeling and Simulation Monitoring data 0202 electrical engineering electronic engineering information engineering Earth and Planetary Sciences (miscellaneous) Kernel extreme learning machine 020201 artificial intelligence & image processing Data mining Engineering (miscellaneous) computer |
| Zdroj: | Journal of Applied Geodesy. 14:253-261 |
| ISSN: | 1862-9024 1862-9016 |
| Popis: | Bridges are critical to economic and social development of a country. In order to ensure the safe operation of bridges, it is of great significance to accurately predict displacement of monitoring points from bridge Structural Health System (SHM). In the paper, a CEEMDAN-KELM model is proposed to improve the accuracy of displacement prediction of bridge. Firstly, the original displacement monitoring time series of bridge is accurately and effectively decomposed into multiple components called intrinsic mode functions (IMFs) and one residual component using a method named complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Then, these components are forecasted by establishing appropriate kernel extreme learning machine (KELM) prediction models, respectively. At last, the prediction results of all components including residual component are summed as the final prediction results. A case study using global navigation satellite system (GNSS) monitoring data is used to illustrate the feasibility and validity of the proposed model. Practical results show that prediction accuracy using CEEMDAN-KELM model is superior to BP neural network model, EMD-ELM model and EMD-KELM model in terms of the same monitoring data. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|