Prediction and Optimization of Blasting-Induced Ground Vibration in Open-Pit Mines Using Intelligent Algorithms

Guo, Jiang; Zhao, Peidong; Ping-feng, LI

doi:10.3390/app13127166

Cited by 3 publications

(2 citation statements)

References 47 publications

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“…It should be noted that blasting operations are a controllable source of vibrations, whereas mining tremors are random events, mostly associated with rock mass relaxation. An important distinction between these sources is the ability to determine the occurrence time and forecast the intensity of the impact [7][8][9][10][11]. In the case of blasting operations, research is conducted on predicting the impact of induced vibrations on the surrounding environment since the introduction of large quantities of explosive material [12,13].…”

Section: Introductionmentioning

confidence: 99%

“…Several methods and models have been developed to predict and optimize blast-induced ground vibrations [25][26][27][28]. Guo and Li proposed a hybrid intelligent model using a least-squares support vector machine (LSSVM) optimized with a particle swarm algorithm (PSO) for vibration prediction [10,29]. The occurrence of mining tremors in underground mines is due to mechanical stress in the rock mass caused by exploitation activities [30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

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The Influence of Vibrations Induced by Blasting Works in an Open-Pit Mine and Seismic Events in an Underground Mine on Building Structures—A Case Study

Sołtys,

Pyra

2024

Applied Sciences

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Monitoring induced vibrations caused by blasting works is becoming an increasingly common form of preventive activity conducted in open-pit mines. Measurement stations also record other events unrelated to blasting works. This article presents a comparison of the intensity of vibrations induced by blasting works in an open-pit mine and mining tremors in an underground mine. The recorded data and conducted analyses of vibration intensity and frequency structure also allowed for a comparison of the impact of vibrations on a building structure. Calculations and analyses, conducted in accordance with the procedures provided in the standard PN-B-02170:2016-12 and the rules for applying the Mining Seismic Intensity Scale MSIS-2017, demonstrated a stronger impact on the building from induced vibrations in an underground mine located 10 km away compared to vibrations induced by blasting operations conducted in an open-pit mine, which is approximately 600 m away from the building. The presented material constitutes a unique set of data that can be used to introduce any necessary corrections in the methodology of analyzing vibrations regarding their harmfulness to building structures. The velocity value of vibrations correlated with frequency alone, without taking into account the vibration duration, can lead to incorrect interpretation.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Influence of Vibrations Induced by Blasting Works in an Open-Pit Mine and Seismic Events in an Underground Mine on Building Structures—A Case Study

Sołtys,

Pyra

2024

Applied Sciences

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State-of-the-art review of machine learning and optimization algorithms applications in environmental effects of blasting

Zhou,

Zhang,

Qiu

2024

Artif Intell Rev

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The technological difficulties related with blasting operations have become increasingly significant. It is crucial to give due consideration to the evaluation of rock fragmentation and the threats posed by environmental effect of blasting (EEB). To address these challenges, numerous scholars have conducted extensive research employing various assessment techniques with the aim of mitigating risks and preventing the emergence of unfavorable EEB. The occurrence of EEB is prevalent during the excavation of hard rock, and it presents significant hazards to personnel safety, equipment integrity, and operational continuity. Therefore, conducting a systematic review of EEB is of utmost importance as it enables a comprehensive understanding of the contributing factors. Such an understanding plays a vital role in advancing EEB prediction and prevention methods. The careful selection of an appropriate EEB assessment method is a crucial aspect of blasting operations. However, there is a lack of comprehensive discussions on the applications of machine learning (ML) and optimization algorithms (OA) in addressing various EEB. Only a limited number of papers have briefly touched upon this topic. Therefore, the primary objective of this paper is to bridge this gap by conducting an analysis of global trends using Cit-eSpace and VOSviewer software from the year 2000 onwards. It comprehensively explores EEB classification and definition, encompassing air overpressure (AOp), ground vibration, dust, backbreak, flyrock, and rock fragmentation. Furthermore, the paper provides a compendium of the most recent ML and OA prediction techniques used to addresses EEB. Finally, the paper concludes by proposing future directions for exploring innovative approaches that combine data-driven ML techniques with knowledge-based or physicsbased methods. Such integration has the potential to mitigate hazards during blasting operations and reduce the likelihood of unfavorable EEB occurrences.

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Prediction and Optimization of Open-Pit Mine Blasting Based on Intelligent Algorithms

Guo,

Zhao,

Zhao

et al. 2024

Applied Sciences

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Blasting prediction and parameter optimization can effectively improve blasting effectiveness and control production energy consumption. However, the presence of multiple factors and diverse effects in open-pit blasting increases the difficulty of effective prediction and optimization. Therefore, this study takes blasting fragmentation as the prediction indicator and proposes a hybrid intelligent model based on multiple parameters. The model employs a least squares support vector machine (LSSVM) optimized by a genetic algorithm (GA) for prediction. Additionally, the performance of GA-LSSVM was compared with LSSVM optimized by rime optimization algorithms (RIME-LSSVM) and by particle swarm optimization algorithms (PSO-LSSVM), unoptimized LSSVM, and the Kuz–Ram empirical model. Furthermore, considering both blasting fragmentation and blasting cost, a multi-objective particle swarm optimization (MOPSO) algorithm was used for blasting parameter optimization, followed by field validation. The results indicated that the GA-LSSVM model provided the best prediction of blasting fragmentation, achieving optimal evaluation metrics: a root mean square error (RMSE) of 1.947, a mean absolute error (MAE) of 1.688, and a correlation coefficient (r) of 0.962. Moreover, the MOPSO optimization model yielded the optimal blasting parameter combination: a burden of 5.5 m, spacing of 4.3 m, specific charge of 0.51 kg/m3, and subdrilling of 2.0 m. Field blasting tests confirmed the reliability of these parameters. This study can provide scientific recommendations for open-pit mine blasting design and cost control.

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Prediction and Optimization of Blasting-Induced Ground Vibration in Open-Pit Mines Using Intelligent Algorithms

Cited by 3 publications

References 47 publications

The Influence of Vibrations Induced by Blasting Works in an Open-Pit Mine and Seismic Events in an Underground Mine on Building Structures—A Case Study

The Influence of Vibrations Induced by Blasting Works in an Open-Pit Mine and Seismic Events in an Underground Mine on Building Structures—A Case Study

State-of-the-art review of machine learning and optimization algorithms applications in environmental effects of blasting

Prediction and Optimization of Open-Pit Mine Blasting Based on Intelligent Algorithms

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