To evaluate the operator health risk exposed to whole-body vibration(WBV) while the electric-shovel loads the ore on the truck body, the semi-truck mathematical model and 3-D virtual prototype were built to simulate t...To evaluate the operator health risk exposed to whole-body vibration(WBV) while the electric-shovel loads the ore on the truck body, the semi-truck mathematical model and 3-D virtual prototype were built to simulate the high shockwave of truck cab under the shovel loading. Discrete element method was utilized to accurately estimate the impacting force on the truck body. Based on the ISO 2631-5 criteria, the Sed is about 0.56 MPa in both models, which means that the dump operators have a high probability of adverse health effects over long-term exposure to these vibrations. The 4-DOF operator model was built to investigate the biodynamic response of seated-human body exposed to WBV in terms of the transmission of vibrations through the body. The results show that the response peak is in the frequency range of 4-6 Hz corresponding to the primary body resonant frequency.展开更多
The nature of rock fragmentation affects the downstream mining processes like loading, hauling, and crushing the blasted rock. Therefore, it is important to evaluate rock fragmentation after blasting for choosing or d...The nature of rock fragmentation affects the downstream mining processes like loading, hauling, and crushing the blasted rock. Therefore, it is important to evaluate rock fragmentation after blasting for choosing or designing optimal strategies for these processes. However, current techniques of rock fragmentation analysis such as sieving, image-based analysis, empirical methods or artificial intelligence-based methods entail different practical challenges, for example, excessive processing time, higher costs, applicability issues in underground environments, user-biasness, accuracy issues, etc. A classification model has been developed by utilizing image analysis techniques to overcome these challenges. The model was tested on about 7500 videos of load-haul-dump (LHD) buckets with blasted material from Malmberget iron ore mine in Sweden. A Kernel-based support vector machine (SVM) method was utilized to extract frames comprising loaded LHD buckets. Then, the blasted rock in the buckets was classified into five distinct categories using the bagging k-nearest neighbor (KNN) technique. The results showed 99.8% and 89.8% accuracy for kernel-based SVM and bagging KNN classifiers, respectively. The developed framework is efficient in terms of the operation time, cost and practicability for different mines and variate amounts of rock masses.展开更多
基金Project(2006BAB11B03)supported by the National Key Technology Research and Development Program of ChinaProject(Z1011030055010004)supported by Beijing Municipal Science Program of China
文摘To evaluate the operator health risk exposed to whole-body vibration(WBV) while the electric-shovel loads the ore on the truck body, the semi-truck mathematical model and 3-D virtual prototype were built to simulate the high shockwave of truck cab under the shovel loading. Discrete element method was utilized to accurately estimate the impacting force on the truck body. Based on the ISO 2631-5 criteria, the Sed is about 0.56 MPa in both models, which means that the dump operators have a high probability of adverse health effects over long-term exposure to these vibrations. The 4-DOF operator model was built to investigate the biodynamic response of seated-human body exposed to WBV in terms of the transmission of vibrations through the body. The results show that the response peak is in the frequency range of 4-6 Hz corresponding to the primary body resonant frequency.
文摘The nature of rock fragmentation affects the downstream mining processes like loading, hauling, and crushing the blasted rock. Therefore, it is important to evaluate rock fragmentation after blasting for choosing or designing optimal strategies for these processes. However, current techniques of rock fragmentation analysis such as sieving, image-based analysis, empirical methods or artificial intelligence-based methods entail different practical challenges, for example, excessive processing time, higher costs, applicability issues in underground environments, user-biasness, accuracy issues, etc. A classification model has been developed by utilizing image analysis techniques to overcome these challenges. The model was tested on about 7500 videos of load-haul-dump (LHD) buckets with blasted material from Malmberget iron ore mine in Sweden. A Kernel-based support vector machine (SVM) method was utilized to extract frames comprising loaded LHD buckets. Then, the blasted rock in the buckets was classified into five distinct categories using the bagging k-nearest neighbor (KNN) technique. The results showed 99.8% and 89.8% accuracy for kernel-based SVM and bagging KNN classifiers, respectively. The developed framework is efficient in terms of the operation time, cost and practicability for different mines and variate amounts of rock masses.
