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|Title:||Ambient effect filtering using NLPCA-SVR in high-rise buildings||Authors:||Ye, XJ
|Issue Date:||2020||Source:||Sensors, 2 Feb. 2020, v. 20, no. 4, 1143, p. 1-20||Abstract:||The modal frequencies of a structure are affected by continuous changes in ambient factors, such as temperature, wind speed etc. This study incorporates nonlinear principal component analysis (NLPCA) with support vector regression (SVR) to build a mathematical model to reflect the correlation between ambient factors and modal frequencies. NLPCA is first used to eliminate the high correlation among different ambient factors and extract the nonlinear principal components. The extracted nonlinear principal components are input into the SVR model for training and predicting. The proposed method is verified by the measured data provided in the Guangzhou New TV Tower (GNTVT) Benchmark. The grid search method (GSM), genetic algorithm (GA) and fruit fly optimization algorithm (FOA) are applied to determine the optimal hyperparameters for the SVR model. The optimized result of FOA is most suitable for the NLPCA-SVR model. As evaluated by the hypothesis test and goodness-of-fit test, the results show that the proposed method has a high generalization performance and the correlation between the ambient factor and modal frequency can be strongly reflected. The proposed method can effectively eliminate the effects of ambient factors on modal frequencies.||Keywords:||Ambient effects
Guangzhou New TV Tower
Nonlinear principal component analysis
Support vector regression
|Publisher:||Molecular Diversity Preservation International (MDPI)||Journal:||Sensors||EISSN:||1424-8220||DOI:||10.3390/s20041143||Rights:||© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
The following publication Ye, X.; Wu, Y.; Zhang, L.; Mei, L.; Zhou, Y. Ambient Effect Filtering Using NLPCA-SVR in High-Rise Buildings. Sensors 2020, 20, 1143 is available at https://dx.doi.org/10.3390/s20041143
|Appears in Collections:||Journal/Magazine Article|
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Citations as of Jul 12, 2020
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