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Machine Learning Algorithms for Data Enrichment: A Promising Solution for Enhancing Accuracy in Predicting Blast-Induced Ground Vibration in Open-Pit Mines

Nguyen Hoang, Bui Xuan-Nam, Drebenstedt Carsten

Inżynieria Mineralna

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2023

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no. 2, vol. 1

79--88

EN

The issue of blast-induced ground vibration poses a significant environmental challenge in open-pit mines, necessitating precise prediction and control measures. While artificial intelligence and machine learning models hold promise in addressing this concern, their accuracy remains a notable issue due to constrained input variables, dataset size, and potential environmental impact. To mitigate these challenges, data enrichment emerges as a potential solution to enhance the efficacy of machine learning models, not only in blast-induced ground vibration prediction but also across various domains within the mining industry. This study explores the viability of utilizing machine learning for data enrichment, with the objective of generating an augmented dataset that offers enhanced insights based on existing data points for the prediction of blast-induced ground vibration. Leveraging the support vector machine (SVM), we uncover intrinsic relationships among input variables and subsequently integrate them as supplementary inputs. The enriched dataset is then harnessed to construct multiple machine learning models, including k-nearest neighbors (KNN), classification and regression trees (CART), and random forest (RF), all designed to predict blast-induced ground vibration. Comparative analysis between the enriched models and their original counterparts, established on the initial dataset, provides a foundation for extracting insights into optimizing the performance of machine learning models not only in the context of predicting blast-induced ground vibration but also in addressing broader challenges within the mining industry.

2

Artificial Neural Network Optimized by Modified Particle Swarm Optimization for Predicting Peak Particle Velocity Induced by Blasting Operations in Open Pit Mines

Bui Xuan‑Nam, Nguyen Hoang, Nguyen Truc Anh

Inżynieria Mineralna

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2021

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no. 2

79--90

EN

Blasting is an indispensable part of the open pit mining operations. It plays a vital role in preparing the rock mass for subsequent operations, such as loading/unloading, transporting, crushing, and dumping. However, adverse effects, especially blast-induced ground vibrations, are considered one of the most dangerous problems. In this study, artificial intelligence was supposed to predict the intensity of blast-induced ground vibration, which is represented by the peak particle velocity (PPV). Accordingly, an artificial neural network was designed to predict PPV at the Coc Sau open pit coal mine with 137 blasting events were collected. Aiming to optimize the ANN model, the modified version of the particle swarm optimization (MPSO) algorithm was applied to optimize the ANN model for predicting PPV, called the MPSO-ANN model. For the comparison purposes, two forms of empirical equations, namely United States Bureau of Mining (USBM) and U Langefors - Kihlstrom, were also developed to predict PPV and compared with the proposed MPSO-ANN model. The results showed that the proposed MPSO-ANN model provided a better performance with a mean absolute error (MAE) of 1.217, root-mean-squared error (RMSE) of 1.456, and coefficient of determination (R2) of 0.956. Meanwhile, the empirical models only provided poorer performances with an MAE of 1.830 and 2.012, RMSE of 2.268 and 2.464, and R2 of 0.874 and 0.852 for the USBM and U Langefors – Kihlstrom empirical models, respectively.

3

Prediction of blast-induced ground vibration using GPR and blast-design parameters optimization based on novel grey-wolf optimization algorithm

Lawal A. I., Olajuyi S. I., Kwon S., Aladejare A. E., Edo T. M.

Acta Geophysica

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2021

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Vol. 69, no. 4

1313--1324

EN

Blasting is an intrinsic component of mining cycle of operation. However, it is usually associated with negative environmental efects such as blast-induced ground vibration (BIGV) which require accurate prediction and control. Therefore, in this study, Gaussian process regression (GPR) has been proposed for prediction of BIGV in terms of peak particle velocity (PPV), while grey-wolf optimization (GWO) algorithm has been used to optimize the blast-design parameters for the control of BIGV in Obajana limestone quarry, Nigeria. The blast-design parameters such as burden (B), spacing (S), hole depth (Hd), stemming length (T), and number of holes (nh) were obtained from the quarry. The distance from the blasting point to the measuring point (D) and the charge per delay (W) were measured and determined, respectively. The PPV was also measured for the number of blasting operations witnessed. These seven parameters were used as inputs to the proposed GPR model, while the PPV was the targeted output. The performance of the proposed model was evaluated using some statistical indices. The output of the GPR model was compared with ANN model and three empirical models, and the GPR model proved to be more accurate with the coefcient of determination (R2 ) of approximately 1 and variance accounted for VAF of about 100%, respectively. In addition, the GWO was also developed to select the optimum blasting parameters using the ANN model for the generation of objective function. The output of the GWO revealed that if the number of holes (nh) can be reduced by 45% and W by 8%, the PPV will be reduced by about 94%. Hence, the proposed models are both suitable for prediction of PPV and optimization of blast-design parameters.

4

Effect of Blast-induced Ground Vibration on Factor of Safety of Pit Wall Stability

Jaroonpattanapong P., Pantachang K., Thungfung S., Petthong N.

Inżynieria Mineralna

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2020

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no. 2, vol. 1

147--154

EN

The regulated maximum peak particle velocity (PPV) from blasting operations of an open-pit coal mine is less than 2 mm/s to prevent mainly any public disturbance such as ground vibration and air blast. However, the blast-induce ground vibration can also decrease the stability of pit slope, which has not been intensively studied. A claystone pit wall, which is geotechnically investigated as having a plane failure type and the natural condition factor of safety (FS), has been selected for this study. The FS is selected to measure the effect of blast-induced ground vibration on the slope stability. The limit equilibrium, pseudo-static 1 (), and pseudo-static 2 () methods are used to determine the FS. The vibration results of blasting monitored at three slope positions: crest, middle, and toe, from two areas at the same pit wall, are recorded by blasting seismographs. Maximum charge weight per delay and the distance from blast areas to seismographs are collected to construct the scaled distance. The percentage change of FS of three methods from both areas compared to natural condition FS are all less than 4 percent considered that the slope stability is safe from blasting vibration (less than 15 percent). The relationship between the FS and maximum PPV from the limit equilibrium, pseudo-static 1 (), and pseudo-static 2 () methods indicate that the adverse maximum PPVs given the unity FS are 16.60 and 4.58, and 4.74 mm/s, respectively. The regulated PPV less than 2 mm/s at the mine is reasonable to prevent any possible plane failure. However, many impact parameters have not been included in this study, and their effects may disturb the pit wall stability.

PL

Regulowana maksymalna szczytowa prędkość cząstek (PPV) z operacji wybuchowych w kopalni odkrywkowej wynosi mniej niż 2 mm / s, aby zapobiec głównie wszelkim zakłóceniom społecznym, takim jak wibracje gruntu i podmuch powietrza. Jednak wibracje gruntu wywołane podmuchami mogą również zmniejszyć stabilność zbocza wykopu, co nie było intensywnie badane. Do badania wybrano ścianę iłowca, która została zbadana geotechnicznie jako mająca typ zniszczenia płaskiego i znana jako naturalny współczynnik bezpieczeństwa (FS). FS jest wybierany do pomiaru wpływu wibracji gruntu wywołanych podmuchami na stabilność zbocza. Równowaga graniczna, metody pseudo-statyczne 1 (kH) i pseudostatyczne 2 (kH, kv) są używane do wyznaczania FS. Wyniki drgań robót strzałowych monitorowane w trzech położeniach zboczy: w wierzchołku, w środku i na palcach z dwóch obszarów na tej samej ścianie wykopu są rejestrowane za pomocą sejsmografów strzałowych. Maksymalny ciężar ładunku na opóźnienie i odległość od obszarów wybuchu do sejsmografów są zbierane w celu obliczenia wyskalowanej odległości. Procentowa zmiana FS trzech metod z obu obszarów w porównaniu ze stanem naturalnym FS wynosi mniej niż 4 procent, co oznacza, że stabilność zbocza jest bezpieczna przed drganiami wybuchowymi (mniej niż 15 procent). Zależność między FS i maksymalnym PPV z równowagi granicznej, pseudo- statyczna 1 (kH) i pseudo-statyczna 2 (kH, kv) wskazuje, że niekorzystne maksymalne PPV przy jednostkowej FS wynoszą 16,60 i 4,58 oraz 4,74 mm / s, odpowiednio. Regulowany PPV poniżej 2 mm / s w kopalni jest rozsądnym rozwiązaniem, aby zapobiec możliwej awarii. Jednak wiele parametrów uderzenia nie zostało uwzględnionych w tym badaniu, a ich wpływ może naruszyć stabilność zboczy odkrywki.