Intelligent Technique for Prediction of Blast Fragmentation Due to the Blasting in Tropically Weathered Limestone

Bhatawdekar, Ramesh Murlidhar; Kumar, Deepak; Changtham, Saksarid; Pathak, Devanshu; TrilokNath, Singh; Mohamad, Edy Tonnizam

doi:10.1007/978-981-16-9770-8_53

Cited by 4 publications

(1 citation statement)

References 17 publications

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“…More over when identical blast geometry and explosive energy input are applied to two different rock masses, they will result in very varied levels of fragmentation [10]. Literature explained that due to the fact that each rock mass has a unique propensity to resist being broken up by blasting, a property known as blastability [11,12].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Rock and Explosive Properties for Fragmentation Characterization: An Application of WipFrag

Otokiti,

Adebayo

2024

IJEATS

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This study investigates the rock explosive properties in selected Lokoja quarries, Nigeria, with the goal of characterizing fragmentation for optimized downstream operations. The analysis includes porosity, UCS values, and permeability assessment in Gitto quarry, highlighting advantages in material application. Comparisons between rock formations (Q1 and Q2) reveal varying compressive strengths, crucial for determining appropriate explosive energy for efficient fragmentation. Blast design parameters from Q1 and Q2 indicate consistent values, aiding in operational planning. Fragmentation analysis, conducted using WipFrag software, delineates size ranges and classifies the blast as having a moderate distribution. Correlations between blast fragmentation size and powder factor underscore the impact on efficiency. A classification chart and table are presented for convenient interpretation of results, providing valuable insights for enhancing blasting practices in Lokoja quarries and ultimately improving productivity. The fragmentation analysis result carried out in this study using WipFrag software shows that the 50%, 80% and Maximum block size passing size ranges from 539.94 – 1349.53 mm, 690.07 – 1907.81 mm, 808-2280 mm respectively. The blast fragmentation sizes are classified as moderate distribution blast based on the uniformity index value ranging from 1.95 to 2.4. The relationship between blast fragmentation size and powder factor was evaluated using linear correlation coefficient. It was noted that, X20, X50, X80 has high R2 values greater than 60% respectively

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Section: Introductionmentioning

confidence: 99%

Evaluation of Rock and Explosive Properties for Fragmentation Characterization: An Application of WipFrag

Otokiti,

Adebayo

2024

IJEATS

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Development of artificial neural network based mathematical models for predicting small scale quarry powder factor for efficient fragmentation coupled with uniformity index model

Taiwo,

Yewuhalashet,

Adamolekun

et al. 2023

Artif Intell Rev

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Blasting powder factor is an important consideration when planning a drilling and blasting operation because it affects both the total production cost and the e ciency of downstream operations. Ten production blasts and 38 blast record datasets from a small scale dolomite quarry in Akoko Edo, Nigeria were studied to determine the effect of powder factor on blast fragmentation size distribution and uniformity index. This research evaluated the relationship between powder factor and fragmentation size distribution, as well as apply arti cial neural network to model small diameter drill hole blast powder factor based on predominant controllable blast design factors. According to the research, the small scale blasting powder factor ranges between 0.6 and 1.0 kg/m 3 . According to the WipFrag analysis, increasing the powder factor causes an increase in the fragmentation mean size and an upward shift in the particle size distribution curve. The mine blasting operation uniformity index was also found to be between 1.3 and 1.68. The powder factor range of 0.7-0.9kg/m 3 was determined by the uniformity index. The performance of the proposed ANN model was evaluated using the correlation coe cient and found to have a high prediction accuracy (R 2 = 0.997). As a result, the proposed ANN models can be used to improve the blast powder factor for real-world applications.

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Enhancing rock fragmentation assessment in mine blasting through machine learning algorithms: a practical approach

Gebretsadik,

Kumar,

Fissha

et al. 2024

Discov Appl Sci

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The optimization of blasting operations greatly benefits from the prediction of rock fragmentation. The main factors that affect fragmentation are rock mass characteristics, blast geometry, and explosive properties. This paper is a step towards the implementation of machine learning and deep learning algorithms for predicting the extent of fragmentation (in percentage) in open pit mining. While various parameters can affect rock fragmentation, this study considers ten among them (i.e., spacing, drill hole diameter, burden, average bench height, powder factor, number of holes, charge per delay, uniaxial compressive strength, specific drilling, and stemming) to train and test the models. However, due to a weak correlation with rock fragmentation, drill diameter, average bench height, compressive strength, stemming, and charge per delay are eliminated to reduce model complexity. A total of 219 data sets having five input features including the number of holes, spacing, burden, specific drilling, and powder factor are used to develop the models. Machine learning models (random forest regression, support vector regression, and XG boost), as well as a deep learning model (neural network regression), are applied to develop a practical way that can optimize the prediction of fragmentation. This study employs performance measures such as R-squared, RMSE, MSE, MAPE, and MAE. The optimization of the model revealed promising results, indicating that the architecture 5-64-32-16-1 exhibits strong performance. Specifically, the model achieved mean squared error (MSE) values of 41.32 and 28.59 on the training and test datasets, respectively. The R2 value for both training and test is 0.83. RFR is also performing well compared to SVR and XG boost with MSE values of 12.37 and 9.89 on training and testing data, respectively.in both sets, the R2 value is 94%. Based on permutation importance and shapely plot values, it is observed that the powder factor has the highest impact, while the burden has the lowest impact on fragmentation.

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Intelligent Technique for Prediction of Blast Fragmentation Due to the Blasting in Tropically Weathered Limestone

Cited by 4 publications

References 17 publications

Evaluation of Rock and Explosive Properties for Fragmentation Characterization: An Application of WipFrag

Evaluation of Rock and Explosive Properties for Fragmentation Characterization: An Application of WipFrag

Development of artificial neural network based mathematical models for predicting small scale quarry powder factor for efficient fragmentation coupled with uniformity index model

Enhancing rock fragmentation assessment in mine blasting through machine learning algorithms: a practical approach

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