Ambient Effect Filtering Using NLPCA-SVR in High-Rise Buildings
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 betwee...
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MDPI AG
2020-02-01
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Online Access: | https://www.mdpi.com/1424-8220/20/4/1143 |
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author | Xijun Ye Yingfeng Wu Liwen Zhang Liu Mei Yunlai Zhou |
author_facet | Xijun Ye Yingfeng Wu Liwen Zhang Liu Mei Yunlai Zhou |
author_sort | Xijun Ye |
collection | DOAJ |
description | 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. |
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language | English |
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spelling | doaj.art-56cc8da98dcb4c8d810f3d795270bab52022-12-22T02:14:29ZengMDPI AGSensors1424-82202020-02-01204114310.3390/s20041143s20041143Ambient Effect Filtering Using NLPCA-SVR in High-Rise BuildingsXijun Ye0Yingfeng Wu1Liwen Zhang2Liu Mei3Yunlai Zhou4School of Civil Engineering, Guangzhou University, Guangzhou 510006, ChinaSchool of Civil Engineering, Guangzhou University, Guangzhou 510006, ChinaSchool of Civil Engineering, Guangzhou University, Guangzhou 510006, ChinaGuangdong Provincial Key Laboratory of Durability for Marine Civil Engineering, Shenzhen University, Shenzhen 518060, ChinaDepartment of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, SAR, ChinaThe 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.https://www.mdpi.com/1424-8220/20/4/1143ambient effectsmodal frequencyguangzhou new tv towernonlinear principal component analysissupport vector regression |
spellingShingle | Xijun Ye Yingfeng Wu Liwen Zhang Liu Mei Yunlai Zhou Ambient Effect Filtering Using NLPCA-SVR in High-Rise Buildings Sensors ambient effects modal frequency guangzhou new tv tower nonlinear principal component analysis support vector regression |
title | Ambient Effect Filtering Using NLPCA-SVR in High-Rise Buildings |
title_full | Ambient Effect Filtering Using NLPCA-SVR in High-Rise Buildings |
title_fullStr | Ambient Effect Filtering Using NLPCA-SVR in High-Rise Buildings |
title_full_unstemmed | Ambient Effect Filtering Using NLPCA-SVR in High-Rise Buildings |
title_short | Ambient Effect Filtering Using NLPCA-SVR in High-Rise Buildings |
title_sort | ambient effect filtering using nlpca svr in high rise buildings |
topic | ambient effects modal frequency guangzhou new tv tower nonlinear principal component analysis support vector regression |
url | https://www.mdpi.com/1424-8220/20/4/1143 |
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