<p>Blasting operations in open-cast mines, while essential for mineral extraction, can generate significant peak particle velocity (PPV), posing environmental and structural risks. Accurate PPV prediction is critical to mitigate these effects and optimize blasting practices. This review introduces a hybrid ML approach that combines traditional methods, such as decision trees and SVMs, with advanced techniques like ensemble learning and neural networks. The performance of these models is evaluated based on blast parameters, geographical conditions, and monitoring data. The study highlights that hybrid and ensemble methods outperform other techniques in the majority of cases, especially in surface blasting scenarios. The increasing use of these advanced methods underscores their potential to address key challenges in blasting operations. Hybrid machine learning models over traditional methods by combining the strengths of multiple algorithms, effectively reducing bias and variance while enhancing predictive accuracy. Unlike conventional models, which often struggle with nonlinear relationships and high-dimensional data, hybrid approaches leverage advanced feature engineering, ensemble learning, and optimization techniques to improve robustness and generalization. In our study, these models demonstrated superior reliability in predicting PPV, achieving higher accuracy in terms of RMSE and <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="10462_2025_11156_Article_IEq1.gif" Format="GIF" Height="17" Rendition="HTML" Resolution="72" Type="Linedraw" Width="19" /> </InlineMediaObject> <EquationSource Format="TEX">\(\text {R}^2\)</EquationSource> </InlineEquation>. By combining different techniques, they mitigate individual model weaknesses, reduce errors, and improve feature selection. In addition, hybrid models prevent overfitting and optimize predictions through ensemble strategies such as boosting and stacking. This study explores the advantages of hybrid ML models, demonstrating their superior performance compared to conventional approaches. The review also identifies gaps in research on underground blasting and suggests future directions, emphasizing the importance of ongoing technological advancements and industry awareness of ML techniques benefits. Enhanced accuracy and robustness in PPV prediction, driven by hybrid approaches and real-time systems, are crucial to improve safety and efficiency in mining operations.</p>

A systematic survey of hybrid ML techniques for predicting peak particle velocity (PPV) in open-cast mine blasting operations

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2025年4月11日

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Gundaveni Shylaja,
Ragam Prashanth

摘要

Blasting operations in open-cast mines, while essential for mineral extraction, can generate significant peak particle velocity (PPV), posing environmental and structural risks. Accurate PPV prediction is critical to mitigate these effects and optimize blasting practices. This review introduces a hybrid ML approach that combines traditional methods, such as decision trees and SVMs, with advanced techniques like ensemble learning and neural networks. The performance of these models is evaluated based on blast parameters, geographical conditions, and monitoring data. The study highlights that hybrid and ensemble methods outperform other techniques in the majority of cases, especially in surface blasting scenarios. The increasing use of these advanced methods underscores their potential to address key challenges in blasting operations. Hybrid machine learning models over traditional methods by combining the strengths of multiple algorithms, effectively reducing bias and variance while enhancing predictive accuracy. Unlike conventional models, which often struggle with nonlinear relationships and high-dimensional data, hybrid approaches leverage advanced feature engineering, ensemble learning, and optimization techniques to improve robustness and generalization. In our study, these models demonstrated superior reliability in predicting PPV, achieving higher accuracy in terms of RMSE and \(\text {R}^2\) . By combining different techniques, they mitigate individual model weaknesses, reduce errors, and improve feature selection. In addition, hybrid models prevent overfitting and optimize predictions through ensemble strategies such as boosting and stacking. This study explores the advantages of hybrid ML models, demonstrating their superior performance compared to conventional approaches. The review also identifies gaps in research on underground blasting and suggests future directions, emphasizing the importance of ongoing technological advancements and industry awareness of ML techniques benefits. Enhanced accuracy and robustness in PPV prediction, driven by hybrid approaches and real-time systems, are crucial to improve safety and efficiency in mining operations.