Prediction of Penetration Depth of Projectiles into Concrete Targets Based on PSO-SVM

The prediction of the penetration depth of concrete in concrete damage effect is of great significance to the design and construction in protection engineering.However, the traditional methods for this prediciton involve such problems as requiring a great supply of samples, or suffering from a large prediction error, and so on.In this work, following the theory of the support vector machine (SVM) and according to the parameters optimized through the particle swarm optimization (PSO), the PSO-SVM for predicting the penetration depth was proposed.The corresponding programs were written and the prediction was verified by the experiment data.The results show that the PSO-SVM method has a great advantage for small samples and non-linear prediction.In comparison with the traditional grey theory, the relative predicted errors through the PSO-SVM method are smaller (the maximum relative error being 3.18%).As the number of the samples increases, the maximum relative errors decrease and the changing rate slows down whereas, however, the amount of calculation becomes larger.Above all, it is feasible to apply PSO-SVM method to the prediction of penetration depth of projectiles into concrete targets.