| 1 | Predicting rock size distribution in mine blasting using various novel soft computing models based on meta-heuristics and machine learning algorithms显示文摘Blasting is well-known as an effective method for fragmenting or moving rock in open-pit mines.To evaluate the quality of blasting,the size of rock distribution is used as a critical criterion in blasting operations.A high percentage of oversized rocks generated by blasting operations can lead to economic and environmental damage.Therefore,this study proposed four novel intelligent models to predict the size of rock distribution in mine blasting in order to optimize blasting parameters,as well as the efficiency of blasting operation in open mines.Accordingly,a nature-inspired algorithm(i.e.,firefly algorithm-FFA)and different machine learning algorithms(i.e.,gradient boosting machine(GBM),support vector machine(SVM),Gaussian process(GP),and artificial neural network(ANN))were combined for this aim,abbreviated as FFA-GBM,FFA-SVM,FFA-GP,and FFA-ANN,respectively.Subsequently,predicted results from the abovementioned models were compared with each other using three statistical indicators(e.g.,mean absolute error,root-mean-squared error,and correlation coefficient)and color intensity method.For developing and simulating the size of rock in blasting operations,136 blasting events with their images were collected and analyzed by the Split-Desktop software.In which,111 events were randomly selected for the development and optimization of the models.Subsequently,the remaining 25 blasting events were applied to confirm the accuracy of the proposed models.Herein,blast design parameters were regarded as input variables to predict the size of rock in blasting operations.Finally,the obtained results revealed that the FFA is a robust optimization algorithm for estimating rock fragmentation in bench blasting.Among the models developed in this study,FFA-GBM provided the highest accuracy in predicting the size of fragmented rocks.The other techniques(i.e.,FFA-SVM,FFA-GP,and FFA-ANN)yielded lower computational stability and efficiency.Hence,the FFA-GBM model can be used as a powerful and precise soft computing tool that can be applied to practical engineering cases aiming to improve the quality of blasting and rock fragmentation. | Chengyu Xie Hoang Nguyen Xuan-Nam Bui Yosoon Choi Jian Zhou Thao Nguyen-Trang | 2021 | Geoscience Frontiers2021，12，3： | 3 |
| 2 | 基于改进未确知测度的含软弱夹层巷道围岩稳定性评价显示文摘为了准确评价含软弱夹层巷道围岩稳定性,以改进层次分析法和客观赋权法(CRITIC方法)计算综合权重,应用未确知测度理论(UM)计算未确知测度向量,同时引入集对分析理论(SPA)优化置信度评价过程,建立UM⁃SPA耦合分析模型,并运用该模型对某金矿含软弱夹层巷道围岩稳定性进行评价。结果表明,地应力、软弱夹层抗拉强度和软弱夹层厚度是影响该类巷道稳定性的主要因素;断面D4、D5、D6、D7、D9的集对势为微反势,有向不安全状态偏向趋势;巷道稳定性从大到小排序为:290#水平>150#水平>90#水平。实践验证表明,模型评价结果与实际吻合。 | 任家苇 左宇军 林健云 陈庆港 金开玥 | 2023 | 矿冶工程2023，43，4： | 1 |
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