SVM based sub-classification study of engineering waste glass

Fanjing Liu; Hongyan Jiang; Bin Huang

doi:10.1117/12.2673558

3 April 2023 SVM based sub-classification study of engineering waste glass

Fanjing Liu, Hongyan Jiang, Bin Huang

Proceedings Volume 12599, Second International Conference on Digital Society and Intelligent Systems (DSInS 2022); 1259919 (2023) https://doi.org/10.1117/12.2673558
Event: 2nd International Conference on Digital Society and Intelligent Systems (DSInS 2022), 2022, Chendgu, China

Abstract

With the promotion of urbanization in China, a large amount of construction waste materials are piled up. In order to solve the problem of idle construction waste materials, We takes construction waste glass as an example, and establishes a SVM multi-classification prediction model based on one-to-many from two perspectives of classification prediction accuracy and model sensitivity. The data of chemical composition content in waste glass is divided into training set test set according to the ratio of 8:2 to complete the training and validation of the model. The data were brought into the trained model, resulting in five categories. Finally the model was analysed for sensitivity by Monte Carlo simulation with an accuracy of 95.4%.

Citation Download Citation

Fanjing Liu, Hongyan Jiang, and Bin Huang "SVM based sub-classification study of engineering waste glass", Proc. SPIE 12599, Second International Conference on Digital Society and Intelligent Systems (DSInS 2022), 1259919 (3 April 2023); https://doi.org/10.1117/12.2673558

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