Research on evaluation model of rock failure integrity under complex geological conditions in karst area - Jianbo, Ma; Zhongqi, Wang; En, Yang; Menghua, Liu (Frontiers in Earth Science, 2023)
Blasting lumpiness prediction is one of the most important research contents in engineering blasting. Although the traditional KUZ-RAM model is widely used, it often overestimates the size of blasting. Therefore, the KUZ-RAM model was updated or corrected in this paper by simplifying the difficult problem of statistical burst fragmentation in LS-DYNA. Based on the theory of area measurement method, the fitting mechanism of machine learning is used to study the lumpiness of simulation results. The updated KUZ-RAM model adds a coefficient of 0.623 to the original equation of average lumpiness xm. The linear coefficient R2 between the predicted results and the field blasting results increases from −1.99 to 0.97, which significantly improves the prediction of blasting lumpiness.