Development of support vector regression identification model for prediction of dam structural behaviour

• We construct the SVR models for the prediction of the nonlinear structural behaviour.
• The SVR-NARX models were developed and tested using experimental data.
• The parameters of the SVR models are specified with the trial-and-error method.
• Measured values were compared with predicted values.
• The SVR models for the prediction of the dam displacement have excellent performance.

The paper presents the application of support vector regression (SVR) to accurate forecasting of the tangential displacement of a concrete dam. The SVR nonlinear autoregressive model with exogenous inputs (NARX) was developed and tested using experimental data collected during fourteen years. A total of 573 data were used for training of the SVR model whereas the remaining 156 data were used to test the created model. Performance of a SVR model depends on a proper setting of parameters. The SVR parameters, the kernel function, the regularization parameter and the tube size of ε-insensitive loss function are specified carefully by the trail-and-error method. Efficiency of the SVR model is measured using the Pearson correlation coefficient (r), the mean absolute error (MAE) and the mean square error (MSE). Comparison of the values predicted by the SVR-based NARX model with the experimental data indicates that SVR identification model provides accurate results.