Modeling of Temperature Effect on Modal Frequency of Concrete Beam Based on Field Monitoring Data
Temperature variation has been widely demonstrated to produce significant effect on modal frequencies that even exceed the effect of actual damage. In order to eliminate the temperature effect on modal frequency, an effective method is to construct quantitative models which accurately predict the mo...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2018/8072843 |
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| _version_ | 1832551320098177024 |
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| author | Wenchen Shan Xianqiang Wang Yubo Jiao |
| author_facet | Wenchen Shan Xianqiang Wang Yubo Jiao |
| author_sort | Wenchen Shan |
| collection | DOAJ |
| description | Temperature variation has been widely demonstrated to produce significant effect on modal frequencies that even exceed the effect of actual damage. In order to eliminate the temperature effect on modal frequency, an effective method is to construct quantitative models which accurately predict the modal frequency corresponding to temperature variation. In this paper, principal component analysis (PCA) is conducted on the temperatures taken from all embedded thermocouples for extracting input parameters of regression models. Three regression-based numerical models using multiple linear regression (MLR), back-propagation neural network (BPNN), and support vector regression (SVR) techniques are constructed to capture the relationships between modal frequencies and temperature distributions from measurements of a concrete beam during a period of forty days of monitoring. A comparison with respect to the performance of various optimally configured regression models has been performed on measurement data. Results indicate that the SVR exhibits a better reproduction and prediction capability than BPNN and MLR models for predicting the modal frequencies with respect to nonuniformly distributed temperatures. It is succeeded that temperature effects on modal frequencies can be effectively eliminated based on the optimally formulated SVR model. |
| format | Article |
| id | doaj-art-6a70fcc149b44bdc82cf46d8bde33acd |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-6a70fcc149b44bdc82cf46d8bde33acd2025-02-03T06:01:45ZengWileyShock and Vibration1070-96221875-92032018-01-01201810.1155/2018/80728438072843Modeling of Temperature Effect on Modal Frequency of Concrete Beam Based on Field Monitoring DataWenchen Shan0Xianqiang Wang1Yubo Jiao2College of Transportation, Jilin University, Changchun, Jilin 130025, ChinaJiangsu Transportation Institute, Nanjing 211112, ChinaCollege of Transportation, Jilin University, Changchun, Jilin 130025, ChinaTemperature variation has been widely demonstrated to produce significant effect on modal frequencies that even exceed the effect of actual damage. In order to eliminate the temperature effect on modal frequency, an effective method is to construct quantitative models which accurately predict the modal frequency corresponding to temperature variation. In this paper, principal component analysis (PCA) is conducted on the temperatures taken from all embedded thermocouples for extracting input parameters of regression models. Three regression-based numerical models using multiple linear regression (MLR), back-propagation neural network (BPNN), and support vector regression (SVR) techniques are constructed to capture the relationships between modal frequencies and temperature distributions from measurements of a concrete beam during a period of forty days of monitoring. A comparison with respect to the performance of various optimally configured regression models has been performed on measurement data. Results indicate that the SVR exhibits a better reproduction and prediction capability than BPNN and MLR models for predicting the modal frequencies with respect to nonuniformly distributed temperatures. It is succeeded that temperature effects on modal frequencies can be effectively eliminated based on the optimally formulated SVR model.http://dx.doi.org/10.1155/2018/8072843 |
| spellingShingle | Wenchen Shan Xianqiang Wang Yubo Jiao Modeling of Temperature Effect on Modal Frequency of Concrete Beam Based on Field Monitoring Data Shock and Vibration |
| title | Modeling of Temperature Effect on Modal Frequency of Concrete Beam Based on Field Monitoring Data |
| title_full | Modeling of Temperature Effect on Modal Frequency of Concrete Beam Based on Field Monitoring Data |
| title_fullStr | Modeling of Temperature Effect on Modal Frequency of Concrete Beam Based on Field Monitoring Data |
| title_full_unstemmed | Modeling of Temperature Effect on Modal Frequency of Concrete Beam Based on Field Monitoring Data |
| title_short | Modeling of Temperature Effect on Modal Frequency of Concrete Beam Based on Field Monitoring Data |
| title_sort | modeling of temperature effect on modal frequency of concrete beam based on field monitoring data |
| url | http://dx.doi.org/10.1155/2018/8072843 |
| work_keys_str_mv | AT wenchenshan modelingoftemperatureeffectonmodalfrequencyofconcretebeambasedonfieldmonitoringdata AT xianqiangwang modelingoftemperatureeffectonmodalfrequencyofconcretebeambasedonfieldmonitoringdata AT yubojiao modelingoftemperatureeffectonmodalfrequencyofconcretebeambasedonfieldmonitoringdata |