Notice of RetractionLS-SVR with tuned hyperparameters in dam crack forecasting

Notice of Retraction

After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.

We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.

The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.

This paper deals with the application of least squares support vector regression (LS-SVR) with radial basis function (RBF) kernel in dam crack forecasting. In the process of LS-SVR, we performed the standard grid search and particle swarm optimization (PSO) to tune hyperparameters of LS-SVR. The results demonstrate that our PSO approach can identify optimal or near optimal parameters faster than the exhaustive grid search. Comparison with results from stepwise regression was also included, to evaluate the reliability of applying such a PSO method which avoids doing an exhaustive grid search. We found that our LS-SVR approach is promising in dam crack forecasting, however it cannot be used to extract the crack contributed by water pressure, temperature variation, and aging effect, respectively.