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. The experimental results show that the proposed prediction model is an effective model of structural settlement.
Highlights
With the rapid development of information technology, automatic monitoring of structural deformation has become an important way to ensure structural safety [1,2,3]
Aiming at the poor applicability of a single model and the characteristics of deformation data, this paper proposes a combined prediction model based on Empirical Mode Decomposition, Support Vector Regression, and Wavelet Neural Network (EMD-SVR-WNN)
Performance Evaluation Criteria. e evaluation criteria to measure the error of the prediction results included root mean square error (RMSE), mean absolute error (MAE), mean square percentage error (MSPE), and mean absolute percentage error (MAPE). e RMSE and MAPE are selected as evaluation criteria of EMD-SVR-WNN model in this paper. e formulas are as follows: 4.1
Summary
With the rapid development of information technology, automatic monitoring of structural deformation has become an important way to ensure structural safety [1,2,3]. According to the accurate and effective analysis of the real-time structural monitoring data, the structural deformation prediction model can be effectively established, which is of great significance to ensure the structural safety. E single prediction methods such as regression analysis method, time series analysis [5], grey system theory [6], and artificial intelligence method [7,8,9,10] are commonly used in the structural deformation prediction [11]. Time series analysis requires the data to be linear and stable, and the deformation monitoring data in practical engineering are complex and nonlinear, which will affect the prediction accuracy. Experimental results show that fuzzy time series can be effectively applied to deformation prediction, and the prediction accuracy is improved. In view of the problems of a single model, some improved models [16, 17] and combined models have achieved better results
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