排序方式： 共有11条查询结果，搜索用时 15 毫秒
1.
Chengyu Xie Hoang Nguyen Xuan-Nam Bui Yosoon Choi Jian Zhou Thao Nguyen-Trang 《地学前缘(英文版)》2021,12(3):101108
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. 相似文献
2.
3.
Choi Yosoon Nguyen Hoang Bui Xuan-Nam Nguyen-Thoi Trung Park Sebeom 《Natural Resources Research》2021,30(2):1141-1173
Natural Resources Research - This study aimed to develop and assess the feasibility of different machine learning algorithms for predicting ore production in open-pit mines based on a truck-haulage... 相似文献
4.
National-scale assessment of landslide susceptibility to rank the vulnerability to failure of rock-cut slopes along expressways in Korea 总被引：2，自引：1，他引：1
Jangwon Suh Yosoon Choi Tae-Dal Roh Hyi-Jun Lee Hyeong-Dong Park 《Environmental Earth Sciences》2011,63(3):619-632
The objective of this study is to perform a preliminary national-scale assessment of the landslide susceptibility of rock-cut slopes along expressways in Korea. A geographic information system (GIS) database was compiled based on data from topographical and geological maps, and rock-cut slope data, including the locations of past landslides. Seven factors (i.e., slope height, slope length, slope gradient, upper slope gradient, lithology, distance from nearest fault, and dip direction of slope) were extracted from the GIS database to assess the relationship between each factor and landslide events. Weight of evidence (WOE), analytic hierarchy process (AHP), and fuzzy logic methods, as well as hybrid methods, were used to establish the rating of classes for each factor, weightings for the factors, and to combine multiple factor layers into landslide-susceptibility maps. A comparison of the results obtained using several different methods, based on the area under curve technique, revealed that the WOE method showed the highest accuracy of 74%. The annual cost of traffic congestion resulting from slope failures was evaluated to identify those rock-cut slopes where detailed investigations and landslide warning systems are required. 相似文献
5.
6.
Zhang Xiliang Nguyen Hoang Choi Yosoon Bui Xuan-Nam Zhou Jian 《Natural Resources Research》2021,30(6):4735-4751
Natural Resources Research - Peak particle velocity (PPV) is an important criterion for assessing the risk level of ground vibration induced by mine blasting. Based on this criterion, many efforts... 相似文献
7.
The problem of finding the least-cost path from a source point to a destination point can be dealt with by routing across a continuous surface or routing along a discrete network. The solutions within these two contexts are linked to the use of a raster- or a vector-based least-cost path algorithm. This study presents a technique which integrates raster- and vector-based least-cost path algorithms for determining the least-cost path across a continuous raster surface with discrete vector networks. The technique incorporates ancillary vector data sets that are required to examine the travel cost at each link, connections between nodes, and the representation of intersecting links in the discrete vector network into raster-based least-cost path analysis. The integrated technique presented here is applicable to all-terrain vehicle navigation where a continuous raster surface and discrete vector networks need to be considered simultaneously in order to find least-cost paths. This paper describes the concept behind, and details of, the integrated technique. Applications of the technique with synthetic and real-world data sets are also presented. They provide proof that the technique is effective in finding least-cost paths across a continuous raster surface with discrete vector networks. 相似文献
8.
Yosoon Choi Choon Sunwoo Keith C. Clarke 《International journal of geographical information science》2013,27(12):1541-1567
A new raster‐based GIS model that combines multi‐criteria evaluation and least‐cost path analysis was developed to determine the optimal haulage routes of dump trucks in large scale open‐pit mines. The model logic can consider multiple criteria simultaneously (i.e. speed, water body, ore body, curve, visibility, haul road maintenance) and can rate the adverse factor scores of truck movement using fuzzy membership functions. After establishing the weights of five factors by pairwise comparisons, the average adverse score grid can be generated by the weighted linear combination of factor and constraint scores. New software, called Dump Traveler, was implemented to improve the availability of the developed model. An application to the Roto South pit in the Pasir open‐pit coal mine, Indonesia, showed that the software can provide rational solutions to determine the optimal routes on truck haulage operations. Moreover, the layout of available haul roads can be evaluated to consider the trade‐off between road maintenance costs and the potential for traffic jams. Variations of weights for factors were found to be sensitive to the optimal haulage routes determined by least‐cost path analysis. The software provides both optimal routes on truck haulage operations and approximately estimated travel times along the routes, therefore it can support other truck dispatching software that mainly considers scheduling problems. 相似文献
9.
Bui Xuan-Nam Choi Yosoon Atrushkevich Victor Nguyen Hoang Tran Quang-Hieu Long Nguyen Quoc Hoang Hung-Thang 《Natural Resources Research》2020,29(2):771-790
Natural Resources Research - Predicting and reducing blast-induced ground vibrations is a common concern among engineers and mining enterprises. Dealing with these vibrations is a challenging issue... 相似文献
10.
Smartphones can be utilized in the field of geosciences for various purposes due to their multifaceted abilities that combine both hard- and software features. The unique abilities of smartphones allow new methodologies for the collection and visualization of data that rarely become available in traditional computing platforms. In this study, commercially available smartphone applications (apps) that have been released in geoscience (e.g., geology/soil, minerals and rocks, petroleum and gas) so far were investigated. The apps were categorized based on the extent of smartphone feature usage into (a) basic, standard-feature apps; (b) calculator and referencing apps; and (c) sensing and communication apps. Furthermore, each of these categories was divided into several app groups based on specific features. Representative apps of each specific app group were selected and their characteristics and applicability were examined. Lastly, major limitations regarding smartphone app development and implementation in geoscience and implications for future improvements were discussed. 相似文献