Abstract
Aiming at the complex nonlinear relationship among factors affecting blasting fragmentation, the input weight and hidden layer threshold of ELM (extreme learning machine) were optimized by gray wolf optimizer (GWO) and the prediction model of GWO-ELM blasting fragmentation was established. Taking No. 2 open-pit coal mine of Dananhu as an example, seven factors including the rock tensile strength, compressive strength, hole spacing, row spacing, minimum resistance line, super depth, and specific charge are selected as the input factors of the prediction model. The average size of blasting fragmentation X50 is selected as the output factor of the prediction model and compared with the results of PSO-ELM and ELM. The results show that MAPE of GWO-ELM, PSO-ELM, and ELM are 1.78%, 5.40%, and 10.90%, respectively; their RMSE are 0.007, 0.022, and 0.045, respectively. The ELM model optimized by the gray wolf optimizer is more accurate and has stronger data fitting ability than PSO-ELM and ELM models, and the prediction accuracy of GWO-ELM is much higher than that of PSO-ELM and ELM.
Highlights
It is of great theoretical and practical significance to study the fragmentation distribution of rock blasting
Hasanipanah [16] proposed a new model for forecasting the rock fragmentation using adaptive neuro-fuzzy inference system (ANFIS) in combination with particle swarm optimization (PSO) [17,18,19]
By further demonstrating that the impact of brittleness on fragmentation is physically inherent in the material dimension, we present new mechanistic insights into the blast-induced rock fragmentation via integrated analytical modelling, finite element simulation, and image processing [31, 32]
Summary
It is of great theoretical and practical significance to study the fragmentation distribution of rock blasting. Hasanipanah [16] proposed a new model for forecasting the rock fragmentation using adaptive neuro-fuzzy inference system (ANFIS) in combination with particle swarm optimization (PSO) [17,18,19]. Hasanipanah et al [21] developed a precise equation for predicting flyrock through particle swarm optimization (PSO) approach [21]. Hasanipanah et al [22] developed a novel hybrid artificial neural network (ANN) based on the adaptive musical inspired optimization method to predict blast-induced flyrock [22]. Fattahi and Hasanipanah [26] developed a new integrated intelligent model to approximate flyrock based on an adaptive neuro-fuzzy inference system (ANFIS) in combination with a grasshopper optimization algorithm (GOA) [26]. Erefore, this paper uses GWO to optimize the ELM input weights and hidden layer thresholds to improve the stability and prediction accuracy of the ELM model, so as to establish the GWO-ELM blasting fragmentation prediction model
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