Waste engineering pre-demolition glass is often retained as recycled material to be recycled in subsequent engineering construction. In order to achieve effective recycling of different types of glass, we take the study of high potassium glass and Pb-Ba glass as an example and proposes a hybrid engineering glass clustering model with subjective and objective verification. This model firstly determines the two-dimensional classification index system through cardinality analysis, and then constructs a two-level classification model based on K-Means cluster analysis and hierarchical cluster analysis to qualitatively determine the classification result of glass with a class number of 3. Subsequently, the optimal class number for both high potassium glass and Pb-Ba glass is quantitatively determined to be 3 based on the distance evaluation function. The sensitivity of the hybrid clustering model was found to be high.

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%.