Zohreh Nabavi; Mohammad Mirzehi; Hesam Dehghani; Pedram Ashtari
Abstract
Back-break is one of the adverse effects of blasting, which results in unstable mine walls, high duration, falling machinery, and inappropriate fragmentation. Thus, the economic benefits of the mine are reduced, and safety is severely affected. Back-break can be influenced by various parameters such ...
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Back-break is one of the adverse effects of blasting, which results in unstable mine walls, high duration, falling machinery, and inappropriate fragmentation. Thus, the economic benefits of the mine are reduced, and safety is severely affected. Back-break can be influenced by various parameters such as rock mass properties, blast geometry, and explosive properties. Therefore, during the blasting process, back-break must be accurately predicted, and other production activities must be done to prevent and reduce its adverse effects. In this regard, a hybrid model of extreme gradient boosting (XGB) is proposed for predicting back-break using gray wolf optimization (GWO) and particle swarm optimization (PSO). Additionally, validation of the hybrid model is conducted using XGBoost, gene expression programming (GEP), random forest (RF), linear multiple regression (LMR), and non-linear multiple regression (NLMR) methods. For this purpose, the data obtained from 90 blasting operations in the Chadormalu iron ore mine are collected by considering the parameters of the blast pattern design. According to the results obtained, the performance and accuracy level of hybrid models including GWO-XGB (R2 = 99, RMSE = 0.01, MAE = 0.001, VAF = 0.99, a-20 = 0.98), and PSO-XGB (99, 0.01, 0.001, 0.99, 0.98) are better than the XGBoost (97, 0.185, 0.132, 0.98, 95), GEP (96, 0.233, 0.186, 0.967, 0.935), RF (97, 0.210, 0.156, 0.97, 0.94), LMR (96, 0.235, 0.181, 0.964, 0.92), and NLMR (96, 0.229, 0.177, 0.968, 0.93) models. Notably, the GWO-XGB hybrid model has superior overall performance as compared to the PSO-XGB model. Based on the sensitivity analysis results, hole depth and stemming are the essential effective parameters for back-break.
M. Kamran
Abstract
The blasting operation is an important rock fragmentation technique employed in several foundation engineering disciplines such as mining, civil, tunneling, and road planning. Back-break (BB) is one of the adverse effects caused by the blasting operations that produces several effects including vulnerability ...
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The blasting operation is an important rock fragmentation technique employed in several foundation engineering disciplines such as mining, civil, tunneling, and road planning. Back-break (BB) is one of the adverse effects caused by the blasting operations that produces several effects including vulnerability of mining machinery, bench slope design, and risks to the next blast-patterns due to the eruption of gases from several discontinuities in jointed rock masses. Several techniques have been executed by the researchers in order to predict BB in the blasting operations. However, this is the first work to implement a-state-of-the-art Catboost-based t-distributed stochastic neighbor embedding (t-SNE) approach to predict BB. A total of 62 datasets having 12 influential BB-generating features are collected from genuine blasting patterns. A novel dimensionality depletion technique t-SNE that operates the Kullback-Leibler divergence interpretation is employed to tailor the pioneer exaggeration of the blasting dataset. Then the t-SNE dataset obtained is split into a 70:30 ratio of the training and testing datasets. Finally, the Catboost method is implemented on a low-dimensionality blasting database. The performance evaluation criterion confirms that the BB predictive model is more stable with a goodness of fit = 99.04 in the training dataset, 97.26 in the testing datasets, and could anticipate a more accurate prediction. Moreover, the model presented in this work performs superior to the existing publicly available execution of BB. In summary, this model can be practiced in order to predict BB in several rock engineering practices and mining industry scenarios.
