|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Prediction of blasting mean fragment size using support vector regression combined with five optimization algorithms显示文摘The main purpose of blasting operation is to produce desired and optimum mean size rock fragments.Smaller or fine fragments cause the loss of ore during loading and transportation,whereas large or coarser fragments need to be further processed,which enhances production cost.Therefore,accurate prediction of rock fragmentation is crucial in blasting operations.Mean fragment size(MFS) is a crucial index that measures the goodness of blasting designs.Over the past decades,various models have been proposed to evaluate and predict blasting fragmentation.Among these models,artificial intelligence(AI)-based models are becoming more popular due to their outstanding prediction results for multiinfluential factors.In this study,support vector regression(SVR) techniques are adopted as the basic prediction tools,and five types of optimization algorithms,i.e.grid search(GS),grey wolf optimization(GWO),particle swarm optimization(PSO),genetic algorithm(GA) and salp swarm algorithm(SSA),are implemented to improve the prediction performance and optimize the hyper-parameters.The prediction model involves 19 influential factors that constitute a comprehensive blasting MFS evaluation system based on AI techniques.Among all the models,the GWO-v-SVR-based model shows the best comprehensive performance in predicting MFS in blasting operation.Three types of mathematical indices,i.e.mean square error(MSE),coefficient of determination(R^(2)) and variance accounted for(VAF),are utilized for evaluating the performance of different prediction models.The R^(2),MSE and VAF values for the training set are 0.8355,0.00138 and 80.98,respectively,whereas 0.8353,0.00348 and 82.41,respectively for the testing set.Finally,sensitivity analysis is performed to understand the influence of input parameters on MFS.It shows that the most sensitive factor in blasting MFS is the uniaxial compressive strength. | Enming Li Fenghao Yang Meiheng Ren Xiliang Zhang Jian Zhou Manoj Khandelwal | 2021 | Journal of Rock Mechanics and Geotechnical Engineering2021，13，6： | 3 |
| 2 | The Discovery of ~310 Ma Back-Arc Basin Basalt in the West Junggar,Xinjiang,NW China and its Geological Significance显示文摘Objective Mafic magmas can form in different tectonic settings with various geochemical characteristics depending on their mantle sources. Basalts generated in back-arc basins provide valuable perspectives on mantle structure and composition, on controls for melt generation, and on the sources responsible for arc magma genesis. | ZHI Qian LI Yongjun YANG Gaoxue DUAN Fenghao TONG Lili | 2019 | Acta Geologica Sinica(English Edition)2019，93，2： | 1 |
| 3 | The Nature of the West Junggar Basement:Evidence from Magmatic and Detrital Zircon U-Pb Ages显示文摘Objective West Junggar,which is located in the southwestern segment of the Central Asian Orogenic Belt(CAOB),is an important tectonic unit in the evaluation and examination of the largest continental accretion on Earth,playing a significant role in understanding the tectonic evolution and crustal growth within the orogenic belt(Xiao and Santosh,2014).However,no precise record of ancient continental blocks is found in West Junggar,owing to the extensive coverage and later structural reconstructions. | DUAN Fenghao LI Yongjun ZHI Qian YANG Gaoxue | 2021 | Acta Geologica Sinica(English Edition)2021，95，2： | 0 |
| 4 | LA-ICP-MS Zircon U-Pb Age of Newly Discovered Hatu Tectonic Mélange in the West Junggar, Xinjiang, NW China显示文摘Objective The West Junggar, situated in the southwestern segment of the Central Asian Orogenic Belt, is considered to be an important area for Phanerozoic crustal growth owing to the excellent exposures of diverse rock types and multiple generations of structures and magmatic rocks. Recently, a new tectonic mélange has been identified in the southern West Junggar during geological mapping at a scale of 50000. | DUAN Fenghao LI Yongjun ZHI Qian YANG Gaoxue LUO Xin LI Yuhang | 2020 | Acta Geologica Sinica(English Edition)2020，94，4： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员