STUDY ON FRACTAL GRADATION OF TAILINGS AND KNOWLEDGE BANK OF ITS CEMENTING STRENGTH
尾砂分形级配与胶结强度的知识库研究

Characteristics of tailings gradation are studied by using fractal theory. Experimental data about the fractal gradations of tailings material and their cementing strength from a lot of mines are analyzed. Neural network is used to establish the model of knowledge bank which embodies the relations between strengths of cemented tailings and content of cement,consistence,fractal dimension of porosity and correlation coefficient of fractal dimension. Combining grading method with chaotic optimization,the neural network model achieves rapid training and avoids local minimum when there are a lot of samples to be trained. Research results show that the strengths of cemented tailings increase with the decrease of fractal dimension of porosity and with the increase of the correlation coefficients of fractal dimension. Because fractal dimension of porosity and its correlation coefficient embody the global distribution information of tailing granule,they can be used as a standard of rationality of gradation. According to gradation of tailings,the knowledge bank model can predict the strengths of cemented tailings in different contents of cement or consistence,and guide filling design in mine.