Estimation of blast-induced peak particle velocity using ensemble machine learning algorithms: A case study

Ragam, Prashanth; Komalla, Ashoka Reddy; Kanne, Nikhitha

doi:10.1177/09574565221114662

Cited by 7 publications

(1 citation statement)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The XGBoost ensemble method (Zhang et al, 2022) uses a grade-boosting frame and has been extensively employed in the famed Kaggle contests because of its high efficacy and enough stiffness (Chen and Guestrin, 2016;Ragam et al, 2022). The XGBOOST technique, developed in 2016 by Chen and Guestrin, can efficiently construct boosted trees (Chen and Guestrin, 2016).…”

Section: Xgboost Modelmentioning

confidence: 99%

Estimation of slope stability using ensemble-based hybrid machine learning approaches

Ragam,

Kushal Kumar,

Ajith

et al. 2024

Front. Mater.

Self Cite

View full text Add to dashboard Cite

Mining is one of the most daunting occupations gain the sector since it entails risk at any point in the operation. In its operation, the main focus is on slope stability. To avoid slope failures, work should be performed in line with both the regulations and the safety criteria. Slope stability is essential in mining activities owing to slope failure putting productivity and safety at risk. Prediction of slope failure is difficult because of the complexity of traditional engineering techniques. Through study, recent technologies have helped mining companies predict slope problems quickly and effectively. In this current research, an ensemble of machine learning intelligence algorithms was used to estimate and assess the Factor of Safety (FOS). In Ostapal Chromicte Mine, India, 79 experimental and failure slope occurrences were tracked to gather in-the-moment field data. The available data were split into training and testing sets at random to build algorithms. The five influenced factors such as the unit weight, the friction angle, the cohesiveness, the mining depth, as well as the slope angle used as input variables to estimate the FOS. Selected machine learning techniques such as Multiple Linear Regression (MLR), Decision Tree, Random Forest (RF), eXtreme Gradient Boosting (XGBoost) and ensemble hybrid model combining eXtreme Gradient Boosting and Random Forest (XGBoost-RF) were developed to evaluate the FOS. The validity and efficiency of created models can be evaluated using standard evaluation parameters such as coefficient of determination (R2), root mean square error (RMSE), mean square error (MSE), normalized root mean square error (NRMSE), mean absolute percentage error (MAPE) and mean absolute deviation (MAD). The most precise model to assess the FOS across all models was discovered to be the XGBOOST-RF ensemble model, which had a high R2 of 0.931, MSE of 0.009, NRMSE of 0.069, MAD of 0.037, MAPE of 3.581 and an RMSE of 0.098.

show abstract

Section: Xgboost Modelmentioning

confidence: 99%

Estimation of slope stability using ensemble-based hybrid machine learning approaches

Ragam,

Kushal Kumar,

Ajith

et al. 2024

Front. Mater.

Self Cite

View full text Add to dashboard Cite

show abstract

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

Gebretsadik,

Kumar,

Fissha

et al. 2024

Discov Appl Sci

View full text Add to dashboard Cite

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.

show abstract

Support vector regression optimized by black widow optimization algorithm combining with feature selection by MARS for mining blast vibration prediction

Wang

2023

Measurement

View full text Add to dashboard Cite

Estimation of blast-induced peak particle velocity using ensemble machine learning algorithms: A case study

Cited by 7 publications

References 24 publications

Estimation of slope stability using ensemble-based hybrid machine learning approaches

Estimation of slope stability using ensemble-based hybrid machine learning approaches

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

Support vector regression optimized by black widow optimization algorithm combining with feature selection by MARS for mining blast vibration prediction

Contact Info

Product

Resources

About