Analysis of Blasting Overbreak using Stereo Photogrammetry in an Underground Mine

Lee, Seung-Joong; Choi, Sung-Oong; Lee, Sudeuk; Jeon, Seokwon; Jin, Yeon-Ho; Jung, Min-Su

doi:10.7474/tus.2016.26.5.348

Cited by 3 publications

(3 citation statements)

References 6 publications

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“…Machine learning is in the following serial order: Alir Bank, Bank Gospoderstewa Krjowega, Bank Handloy Warsz, Bank Millennium, machine learning Pocztowy SA, Bank Polka Kasi, BGZ BNP, Gatin Nobel Bank, Idea machine learning, ING machine learning, M machine learning, Pulaski machine learning, and Santander machine learning. The examination incorporated all machine learnings that recorded credit openings by the industry as per the EU CRB-D equation in their yearly reports for 2019. Business machine learnings in the nation show a portfolio arrangement by division (a part is signified by a solitary image and partitions everybody into 21 classifications of exercises) in their yearly reports, considering the PKD 2017 definition presented in 2017 [44]. The examination took a gander at a gathering of machine learnings whose consolidated stores represented 84.90% of all homegrown business machine learnings resources [45].…”

Section: Methodsmentioning

confidence: 99%

Evaluating the impact of COVID-19 on the monetary crisis by machine learning

Mohseni

2023

IJ-ICT

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In this study, machine learning is examined in relation to commercial machine learning's resilience to the COVID-19 pandemic-related crisis. Two approaches are used to assess the pandemic's impact on machine learning risk, as well as a method to prioritize sectors according to the crisis's potential negative consequences. I conducted the study to determine Santander machine learning's resilience. The data mining area offers prospects for COVID-19's future. A total of 13 machine learning demos were selected for its organization. The Hellweg strategy and the technique for order preference by similarity to ideal solution (TOPSIS) technique were utilized as direct request strategies. Parametric assessment of machine learning versatility in business was based on capital sufficiency, liquidity proportion, market benefits, and share in an arrangement of openings with a perceived disability, and affectability of machine learning's credit portfolio to monetary hazard. As a result of the COVID-19 pandemic, these enterprises were ranked according to their threat. Based on the findings of the research, machine learning worked the best for the pandemic. Meanwhile, machine learning suffered the most during the downturn. It can be seen, for example, in conversations about the impact of the pandemic on developing business sector soundness and managing financial framework solidity risk.

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Section: Methodsmentioning

confidence: 99%

Evaluating the impact of COVID-19 on the monetary crisis by machine learning

Mohseni

2023

IJ-ICT

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“…LiDAR, as 3D point cloud data can be easily acquired, has been utilized rapidly and widely in various sectors, including civil engineering, construction, aviation, archeology, geology, and the game and movie industries. In the rock engineering field, LiDAR has been employed for studies on the evaluation of joint roughness [1][2][3][4], trace sampling of joints [5][6][7], analysis of joint orientation [8], analysis of slope stability [9,10], analysis of rockfall and toppling [11,12], analysis of blasting over-break [13,14], and geometry research of underground mines [15]. The utilization of LiDAR is expected to increase in the future in study fields that require quantitative analysis.…”

Section: Introductionmentioning

confidence: 99%

“…In conclusion, investigation of the 3D survey and 3D numerical analysis techniques performed in this study are expected to provide higher reliability than the current techniques used for establishing whether mining plans require new mining methods or safety measures.Sustainability 2019, 11, 945 2 of 20 engineering, construction, aviation, archeology, geology, and the game and movie industries. In the rock engineering field, LiDAR has been employed for studies on the evaluation of joint roughness [1][2][3][4], trace sampling of joints [5][6][7], analysis of joint orientation [8], analysis of slope stability [9,10], analysis of rockfall and toppling [11,12], analysis of blasting over-break [13,14], and geometry research of underground mines [15]. The utilization of LiDAR is expected to increase in the future in study fields that require quantitative analysis.In this study, surveying was conducted using LiDAR to evaluate the stability of the safety pillar and stope in a mine where the hybrid room-and-pillar mining method was used, and the results were utilized directly to perform 3D stability analysis.…”

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confidence: 99%

Analyzing the Stability of Underground Mines Using 3D Point Cloud Data and Discontinuum Numerical Analysis

Lee

Choi

2019

Sustainability

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This study describes a precise numerical analysis process by adopting the real image of mine openings obtained by light detection and ranging (LiDAR), which can produce a point cloud data by measuring the target surface numerically. The analysis target was a section of an underground limestone mine, to which a hybrid room-and-pillar mining method that was developed to improve ore recovery was applied. It is important that the center axis and the volume of the vertical safety pillar in the lower parts match those in the upper parts. The 3D survey of the target section verified that the center axis of the vertical safety pillar in the lower parts had deviated in a north-westerly direction. In particular, the area of the lower part of the vertical safety pillar was approximately 34 m 2 lower than the designed cross-sectional area, which was 100 m 2 . In order to analyze the stability of the vertical safety pillar, a discontinuum numerical analysis and safety factor analysis were conducted using 3D surveying results. The analysis verified that instability was caused by the joints distributed around the vertical safety pillar. In conclusion, investigation of the 3D survey and 3D numerical analysis techniques performed in this study are expected to provide higher reliability than the current techniques used for establishing whether mining plans require new mining methods or safety measures.Sustainability 2019, 11, 945 2 of 20 engineering, construction, aviation, archeology, geology, and the game and movie industries. In the rock engineering field, LiDAR has been employed for studies on the evaluation of joint roughness [1][2][3][4], trace sampling of joints [5][6][7], analysis of joint orientation [8], analysis of slope stability [9,10], analysis of rockfall and toppling [11,12], analysis of blasting over-break [13,14], and geometry research of underground mines [15]. The utilization of LiDAR is expected to increase in the future in study fields that require quantitative analysis.In this study, surveying was conducted using LiDAR to evaluate the stability of the safety pillar and stope in a mine where the hybrid room-and-pillar mining method was used, and the results were utilized directly to perform 3D stability analysis. Overview of the FieldThe target mine in this study, the Daesung MDI Donghae limestone mine, is located at Donghae-si, Gangwon-do, South Korea. Here, a test section of a horizontal cross-section (50 m × 50 m) around the Lv540 stope was excavated and mining work was conducted using the hybrid room-and-pillar mining method. This method is used to maximize ore recovery by collecting ores while ensuring the stability of stope using horizontal and vertical safety pillars. As shown in Figure 1, this mining method consists of a number of distinct steps: a step for gallery excavation (Figure 1a), a step for opening the upper stope (Figure 1b), a step for opening a middle stope (Figure 1c), a step for an additional opening for the upper stope (Figure 1d), a step for opening the lower stope (Figure 1e), and a s...

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