A Prediction Model of Structural Settlement Based on EMD-SVR-WNN
Timely and accurate prediction of structural settlement is of great significance to eliminate the hidden danger of structural and prevent structural safety accidents. Since the deformation monitoring data usually is nonstationary and nonlinear, the deformation prediction is a difficult problem in th...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2020/8831965 |
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| _version_ | 1832566334162993152 |
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| author | Xianglong Luo Wenjuan Gan Lixin Wang Yonghong Chen Xue Meng |
| author_facet | Xianglong Luo Wenjuan Gan Lixin Wang Yonghong Chen Xue Meng |
| author_sort | Xianglong Luo |
| collection | DOAJ |
| description | Timely and accurate prediction of structural settlement is of great significance to eliminate the hidden danger of structural and prevent structural safety accidents. Since the deformation monitoring data usually is nonstationary and nonlinear, the deformation prediction is a difficult problem in the structural monitoring research. Aiming at the problems in the structural deformation prediction model and considering the internal characteristics of deformation monitoring data and the influence of different components in the data on the prediction accuracy, a combined prediction model based on the Empirical Mode Decomposition, Support Vector Regression, and Wavelet Neural Network (EMD-SVR-WNN) is proposed. EMD model is used to decompose the structure settlement monitoring data, and the settlement data can be effectively divided into relatively stable trend terms and residual components of random fluctuation by energy matrix. According to the different characteristics of random items and trend items, WNN and SVR methods are, respectively, used for prediction, and the final settlement prediction is obtained by integrating the prediction results. The measured ground settlement data of foundation pit in subway construction is used to test the performance of the model, and the test results show that the prediction accuracy of the combined prediction model proposed in this paper reaches 99.19%, which is 77.30% higher than the traditional SVR, WNN, and DBN-SVR models. The experimental results show that the proposed prediction model is an effective model of structural settlement. |
| format | Article |
| id | doaj-art-4bb138b384fb4c5d815893aa322dba3e |
| institution | Kabale University |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-4bb138b384fb4c5d815893aa322dba3e2025-02-03T01:04:29ZengWileyAdvances in Civil Engineering1687-80861687-80942020-01-01202010.1155/2020/88319658831965A Prediction Model of Structural Settlement Based on EMD-SVR-WNNXianglong Luo0Wenjuan Gan1Lixin Wang2Yonghong Chen3Xue Meng4School of Information Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Information Engineering, Chang’an University, Xi’an 710064, ChinaChina Railway First Survey and Design Institute Group Co., Ltd., Xi’an 710043, ChinaSchool of Information Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Information Engineering, Chang’an University, Xi’an 710064, ChinaTimely and accurate prediction of structural settlement is of great significance to eliminate the hidden danger of structural and prevent structural safety accidents. Since the deformation monitoring data usually is nonstationary and nonlinear, the deformation prediction is a difficult problem in the structural monitoring research. Aiming at the problems in the structural deformation prediction model and considering the internal characteristics of deformation monitoring data and the influence of different components in the data on the prediction accuracy, a combined prediction model based on the Empirical Mode Decomposition, Support Vector Regression, and Wavelet Neural Network (EMD-SVR-WNN) is proposed. EMD model is used to decompose the structure settlement monitoring data, and the settlement data can be effectively divided into relatively stable trend terms and residual components of random fluctuation by energy matrix. According to the different characteristics of random items and trend items, WNN and SVR methods are, respectively, used for prediction, and the final settlement prediction is obtained by integrating the prediction results. The measured ground settlement data of foundation pit in subway construction is used to test the performance of the model, and the test results show that the prediction accuracy of the combined prediction model proposed in this paper reaches 99.19%, which is 77.30% higher than the traditional SVR, WNN, and DBN-SVR models. The experimental results show that the proposed prediction model is an effective model of structural settlement.http://dx.doi.org/10.1155/2020/8831965 |
| spellingShingle | Xianglong Luo Wenjuan Gan Lixin Wang Yonghong Chen Xue Meng A Prediction Model of Structural Settlement Based on EMD-SVR-WNN Advances in Civil Engineering |
| title | A Prediction Model of Structural Settlement Based on EMD-SVR-WNN |
| title_full | A Prediction Model of Structural Settlement Based on EMD-SVR-WNN |
| title_fullStr | A Prediction Model of Structural Settlement Based on EMD-SVR-WNN |
| title_full_unstemmed | A Prediction Model of Structural Settlement Based on EMD-SVR-WNN |
| title_short | A Prediction Model of Structural Settlement Based on EMD-SVR-WNN |
| title_sort | prediction model of structural settlement based on emd svr wnn |
| url | http://dx.doi.org/10.1155/2020/8831965 |
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