A Prediction Model of Structural Settlement Based on EMD-SVR-WNN. (22nd December 2020)
- Record Type:
- Journal Article
- Title:
- A Prediction Model of Structural Settlement Based on EMD-SVR-WNN. (22nd December 2020)
- Main Title:
- A Prediction Model of Structural Settlement Based on EMD-SVR-WNN
- Authors:
- Luo, Xianglong
Gan, Wenjuan
Wang, Lixin
Chen, Yonghong
Meng, Xue - Other Names:
- Ye Xinyu Academic Editor.
- Abstract:
- Abstract : 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. TheAbstract : 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. … (more)
- Is Part Of:
- Advances in civil engineering. Volume 2020(2020)
- Journal:
- Advances in civil engineering
- Issue:
- Volume 2020(2020)
- Issue Display:
- Volume 2020, Issue 2020 (2020)
- Year:
- 2020
- Volume:
- 2020
- Issue:
- 2020
- Issue Sort Value:
- 2020-2020-2020-0000
- Page Start:
- Page End:
- Publication Date:
- 2020-12-22
- Subjects:
- Civil engineering -- Periodicals
Civil engineering
Periodicals
624 - Journal URLs:
- http://bibpurl.oclc.org/web/50276 ↗
http://rzblx1.uni-regensburg.de/ezeit/warpto.phtml?colors=7&jour_id=109850 ↗
https://www.hindawi.com/journals/ace/ ↗ - DOI:
- 10.1155/2020/8831965 ↗
- Languages:
- English
- ISSNs:
- 1687-8086
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD Digital store
- Ingest File:
- 15711.xml