Prediction of Blast-Induced Ground Vibration Intensity in Open-Pit Mines Using Unmanned Aerial Vehicle and a Novel Intelligence System

2019 ◽  
Vol 29 (2) ◽  
pp. 771-790 ◽  
Author(s):  
Xuan-Nam Bui ◽  
Yosoon Choi ◽  
Victor Atrushkevich ◽  
Hoang Nguyen ◽  
Quang-Hieu Tran ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Ground Vibration ◽  
Open Pit ◽  
Open Pit Mines ◽  
Vibration Intensity ◽  
Intelligence System ◽  
Aerial Vehicle

scholarly journals Continuous Monitoring and Improvement of the Blasting Process in Open Pit Mines Using Unmanned Aerial Vehicle Techniques

2020 ◽  
Vol 12 (17) ◽  
pp. 2801
Author(s):  
Thomas Bamford ◽  
Filip Medinac ◽  
Kamran Esmaeili
Keyword(s):  
Unmanned Aerial Vehicle ◽  
High Speed ◽  
Field Experiments ◽  
Block Size ◽  
Structural Data ◽  
Open Pit ◽  
Open Pit Mines ◽  
Digital Elevation ◽  
Aerial Vehicle ◽  
Technical Manpower

The current techniques used for monitoring the blasting process in open pit mines are manual, intermittent and inefficient and can expose technical manpower to hazardous conditions. This study presents the application of unmanned aerial vehicle (UAV) systems for monitoring and improving the blasting process in open pit mines. Field experiments were conducted in different open pit mines to assess rock fragmentation, blast-induced damage on final pit walls, blast dynamics and the accuracy of blastholes including production and pre-split holes. The UAV-based monitoring was done in three different stages, including pre-blasting, blasting and post-blasting. In the pre-blasting stage, pit walls were mapped to collect structural data to predict in situ block size distribution and to develop as-built pit wall digital elevation models (DEM) to assess blast-induced damage. This was followed by mapping the production blasthole patterns implemented in the mine to investigate drillhole alignment. To monitor the blasting process, a high-speed camera was mounted on the UAV to investigate blast initiation, sequencing, misfired holes and stemming ejection. In the post-blast stage, the blasted rock pile (muck pile) was monitored to estimate fragmentation and assess muck pile configuration, heave and throw. The collected aerial data provide detailed information and high spatial and temporal resolution on the quality of the blasting process and significant opportunities for process improvement. The current challenges with regards to the application of UAVs for blasting process monitoring are discussed, and recommendations for obtaining the most value out of an UAV application are provided.

scholarly journals Experimental Investigation on the Performance of DJI Phantom 4 RTK in the PPK Mode for 3D Mapping Open-Pit Mines

2020 ◽  
Vol 1 (2) ◽  
Author(s):  
Le VAN CANH ◽  
Cao XUAN CUONG ◽  
Nguyen QUOC LONG ◽  
Le THI THU HA ◽  
Tran TRUNG ANH ◽  
...  
Keyword(s):  
Coal Mines ◽  
Daily Basis ◽  
Open Pit ◽  
Surface Model ◽  
Test Field ◽  
Open Pit Mines ◽  
Ground Control Points ◽  
Aerial Vehicle ◽  
Topographic Surveys

Open-pit coal mines’ terrain is often complex and quickly and frequently changes. Therefore, topographic surveys of open-pit mines are undertaken on a daily basis. While these tasks are very time-consuming and costly with traditional methods such as total station and GNSS, the unmanned aerial vehicle (UAV) based method can be more efficient. This method is a combination of the “Structure from motion” (SfM) photogrammetry technique and UAV photogrammetry which has been widely used in topographic surveying. With an increasing popularity of RTK-enabled drones, it is becoming even more powerful method. While the important role of ground control points (GCP) in the accuracy of digital surface model (DSM) generated from images acquired by “traditional” UAVs (not RTK-enabled drones) has been proved in many previous studies, it is not clear in the case of RTK-enabled drones, especially for complex terrain in open-pit coal mines. In this study, we experimentally investigated the influence of GCP regarding its numbers and distribution on the accuracy of DSM generation from images acquired by RTK-enabled drones in open-pit coal mines. In addition, the Post Processing Kinematic (PPK) mode was executed over a test field with the same flight altitude. DSM generation was performed with several block control configurations: PPK only, PPK with one GCP, and PPK with two GCPs. Several positions of GCPs were also examined to test the optimal locations for placing GCPs to achieve accurate DSMs. The results show that the horizontal and vertical accuracy given by PPK only were 9.3 and 84.4 cm, respectively. However, when adding at least one GCP, the accuracy was significantly improved in both horizontal and vertical components, with RMSE for XY and Z ranging between 3.8 and 9.8 cm (with one GCP) and between 3.0 and 5.7 cm (with two GCPs), respectively. Also, the GCPs placed in the deep areas of the open-pit mine could ensure the cm-level accuracy.

scholarly journals Detecting drainage pitfalls in open-pit mines and haul roads using UAV-photogrammetry

DYNA ◽  
2021 ◽  
Vol 88 (216) ◽  
pp. 190-195
Author(s):  
Felipe Dille Benevenuti ◽  
Rodrigo De Lemos Peroni
Keyword(s):  
Three Dimensional ◽  
Open Pit ◽  
Road Maintenance ◽  
Open Pit Mines ◽  
Cycle Times ◽  
Course Material ◽  
Aerial Vehicle ◽  
Haul Road ◽  
Elevation Model

Open-pit mines generally have operational problems such as puddling and inappropriate water flow over haul roads, particularly if located in areas with high rainfall indices. These situations increase truck cycle times, promote rapid deterioration of haul-road wearing-course material, reduce productivity due to downtime and increase road maintenance. In addition, operational costs are raised as the frequency of truck maintenance and tire failures also increase. The use of a high-resolution three-dimensional elevation model, created based on Unmanned Aerial Vehicle (UAV) photogrammetry, has been shown to be an effective technique to detect anomalies in a fast and precise way. With the proposed approach, it is possible to diagnose haul-road conditions after rainfall or to anticipate the potential occurrence of such anomalies before they become a greater problem. This diagnosis can then be used to prioritize maintenance activities in open-pit mines. To describe the methodology, a case study is presented demonstrating and validating the results obtained.

scholarly journals Application of the k - nearest neighbors algorithm for predicting blast - induced ground vibration in open - pit coal mines: a case study

2020 ◽  
Vol 61 (6) ◽  
pp. 22-29
Author(s):  
Hoang Nguyen . ◽  
Keyword(s):  
Artificial Intelligence ◽  
Coal Mine ◽  
Empirical Model ◽  
Ground Vibration ◽  
Nearest Neighbors ◽  
Superior Performance ◽  
Open Pit ◽  
K Nearest Neighbors ◽  
Open Pit Mines

Blasting is considered as one of the most effective methods for rock fragmentation in open - pit mines. However, its side effects are significant, especially blast - induced ground vibration. Therefore, this study aims to develop and apply artificial intelligence in predicting blast - induced ground vibration in open - pit mines. Indeed, the k - nearest neighbors (KNN) algorithm was taken into account and developed for predicting blast - induced ground vibration at the Deo Nai open - pit coal mine (Vietnam) as a case study. An empirical model (i.e., USBM) was also developed to compare with the developed KNN model aiming to highlight the advantage of the KNN model. Accordingly, 194 blasting events were collected and analyzed for this aim. This database was then divided into two parts, 80% for training and 20% for testing. The MinMax scale and 10 - fold cross - validation techniques were applied to improve the accuracy, as well as avoid overfitting of the KNN model. Root - mean - squared error (RMSE) and determination coefficient (R2) were used as the performance metrics for models’ evaluation and comparison purposes. The results indicated that the KNN model yielded better superior performance than those of the USBM empirical model with an RMSE of 1.157 and R2 of 0.967. In contrast, the USBM model only provided a weak performance with an RMSE of 4.205 and R2 of 0.416. With the obtained results, the KNN can be introduced as a potential artificial intelligence model for predicting and controlling blast - induced ground vibration in practical engineering, especially at the Deo Nai open - pit coal mine.

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