Machine Learning-Based Models for Predicting the Depth of Concrete Penetration

Author:

Li Meng,Wu Haijun,Dong Heng,Ren Guang,Zhang Peng,Huang Fenglei

Publisher

Springer International Publishing

Reference41 articles.

1. Zhang, S.X., Zhao, H.D., Han, Z.G.: Method of penetrate result prediction based on PSO-SVM. J. North Univer. China (Nat. Sci. Ed.) 36(2), 6 (2015)

2. Ryan, S., Thaler, S.: Artificial neural networks for characterising Whipple shield performance. Int. J. Impact Eng. 56, 61–70 (2013)

3. Ryan, S., Kandanaarachchi, S., Smith, M.K.: Support vector machines for characterising Whipple shield performance. Proc. Eng. 103, 522–529 (2015)

4. Ryan, S., Thaler, S., Kandanaarachchi, S.: Machine learning methods for predicting the outcome of hypervelocity impact events. Exp. Syst. Appl. 45, 23–39 (2016)

5. Xiong, P.Q.: Research on space debris damage pattern recognition based on neural network technology (Master’s Thesis). Harbin Institute of Technology, China (2012)

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