In the present work, blast-induced air overpressure is estimated by an innovative intelligence system based on the cubist algorithm (CA) and genetic algorithm (GA) with high accuracy, called GA–CA model. Herein, CA initialization model was developed first and the hyper-parameters of the CA model were selected randomly. Subsequently, the GA procedure was applied to perform a global search for the optimized values of the hyper-factors of the CA model. Root-mean-square error (RMSE) is utilized as a compatibility function to determine the optimal CA model with the lowest RMSE. Gaussian process (GP), conditional inference tree (CIT), principal component analysis (PCA), hybrid neural fuzzy inference system (HYFIS) and k-nearest neighbor (k-NN) models are also developed as the benchmark models in order to compare and analyze the quality of the proposed GA–CA algorithm; 164 blasting works were investigated at a quarry mine of Vietnam for this aim. The results revealed that GA significantly improved the performance of the CA model. Based on the statistical indices used for model assessment, the proposed GA–CA model was confirmed as the most superior model as compared to the other models (i.e., GP, CIT, HYFIS, PCA, k-NN). It can be applied as a robust soft computing tool for estimating blast-induced air overpressure.
相似文献Air over-pressure (AOp) is one of the products of blasting operations for rock fragmentation in open-pit mines. It can cause structural vibration, smash glass doors, adversely affect the surrounding environment, and even be fatal to humans. To assess its dangerous effects, seven artificial intelligence (AI) methods for predicting specific blast-induced AOp have been applied and compared in this study. The seven methods include random forest, support vector regression, Gaussian process, Bayesian additive regression trees, boosted regression trees, k-nearest neighbors, and artificial neural network (ANN). An empirical technique was also used to compare with AI models. The degree of complexity and the performance of the models were compared with each other to find the optimal model for predicting blast-induced AOp. The Deo Nai open-pit coal mine (Vietnam) was selected as a case study where 113 blasting events have been recorded. Indicators used for evaluating model performances include the root-mean-square error (RMSE), determination coefficient (R2), and mean absolute error (MAE). The results indicate that AI techniques provide better performance than the empirical method. Although the relevance of the empirical approach was acceptable (R2?=?0.930) in this study, its error (RMSE?=?7.514) is highly significant to guarantee the safety of the surrounding environment. In contrast, the AI models offer much higher accuracies. Of the seven AI models, ANN was the most dominant model based on RMSE, R2, and MAE. This study demonstrated that AI techniques are excellent for predicting blast-induced AOp in open-pit mines. These techniques are useful for blasters and managers in controlling undesirable effects of blasting operations on the surrounding environment.
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