Predicting rock size distribution in mine blasting using various novel soft computing models based on meta-heuristics and machine learning algorithms

Xie, Chengyu; Nguyen, Hoang; Bui, Xuan‐Nam; Choi, Yosoon; Zhou, Jian; Nguyen‐Trang, Thao

doi:10.1016/j.gsf.2020.11.005

Cited by 65 publications

(20 citation statements)

References 64 publications

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“…Further, we observed that most common ML algorithms for blastinduced fragment size predictions include the ANN [159][160][161][162], SVM [104,177], PCA [177], fuzzy inference system [178][179][180], adaptive neuro-fuzzy inference system [177,181,182], bee colony algorithm [162], PSO [183,184], ant colony optimization [185], and gaussian process regression [186]. The ML-based fragment size prediction models performed significantly better than the empirical models [187]. From the literature, many of the proposed models could predict only one blast impact.…”

Section: Discussion and Future Trendsmentioning

confidence: 99%

Advances in Blast-Induced Impact Prediction—A Review of Machine Learning Applications

2021

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Rock fragmentation in mining and construction industries is widely achieved using drilling and blasting technique. The technique remains the most effective and efficient means of breaking down rock mass into smaller pieces. However, apart from its intended purpose of rock breakage, throw, and heave, blasting operations generate adverse impacts, such as ground vibration, airblast, flyrock, fumes, and noise, that have significant operational and environmental implications on mining activities. Consequently, blast impact studies are conducted to determine an optimum blast design that can maximize the desirable impacts and minimize the undesirable ones. To achieve this objective, several blast impact estimation empirical models have been developed. However, despite being the industry benchmark, empirical model results are based on a limited number of factors affecting the outcomes of a blast. As a result, modern-day researchers are employing machine learning (ML) techniques for blast impact prediction. The ML approach can incorporate several factors affecting the outcomes of a blast, and therefore, it is preferred over empirical and other statistical methods. This paper reviews the various blast impacts and their prediction models with a focus on empirical and machine learning methods. The details of the prediction methods for various blast impacts—including their applications, advantages, and limitations—are discussed. The literature reveals that the machine learning methods are better predictors compared to the empirical models. However, we observed that presently these ML models are mainly applied in academic research.

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Section: Discussion and Future Trendsmentioning

confidence: 99%

Advances in Blast-Induced Impact Prediction—A Review of Machine Learning Applications

2021

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“…Set the termination condition of PSO optimization, the number of iterations N, the population size M, the initial value of inertia weight ω, the initial value of acceleration factor c 1 , c 2 , and so on. According to the initial position of each particle, the fitness value F(i) of each particle is obtained by the fitness function, see Equation (18).…”

Section: Pso Optimized Bp Neural Network Algorithmmentioning

confidence: 99%

“…Set the extreme value P gBest of each particle population to the optimal value of the objective function; calculate the fitness value of each particle, if F(i) > P iBest , F(i) replaces P iBest and optimize the group extreme value P gBest ; update the speed and position of particles according to Equations ( 17) and (18) in each iteration; after the termination of PSO optimization, get the optimal weight and threshold of BP neural network; replace the BP neural network to initialize the initial weight and threshold of BP neural network, and input sample data. The BP neural network trains the model in the way of error reverse transmission.…”

Section: Pso Optimized Bp Neural Network Algorithmmentioning

confidence: 99%

“…The results show that the MI model has higher accuracy than the Kuz-Ram and statistical models [16,17]. Based on meta-heuristics and machine learning algorithms, Xie et al predicted the rock size distribution in mine blasting using various novel soft computing models [18]. Bahrami et al implemented artificial neural network method to develop a model to predict rock fragmentation due to blasting in an iron ore mine [19].…”

Section: Introductionmentioning

confidence: 99%

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Parameter Optimization and Fragmentation Prediction of Fan-Shaped Deep Hole Blasting in Sanxin Gold and Copper Mine

Pan

Zhang

et al. 2022

Minerals

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For San-Xin gold and copper mine, deep blasting large block rate is high resulting in difficulty in transporting the ore out; secondary blasting not only increases blasting costs but is more likely to cause the top and bottom plate of the underground to become loose causing safety hazards. Based on the research background of Sanxin gold and copper mine, deep hole blasting parameters were determined by single-hole, variable-hole pitch, and oblique hole blasting tests, further using the inversion method to determine the optimal deep hole blasting parameters. Meanwhile, the PSO-BP neural network method was used to predict the block rate in deep hole blasting. The results of the study showed that the optimal minimum resistance line was 1.24–1.44 m, which was lower than 1.6–1.8 m in the original blasting design, which was one of the reasons for the higher blasting block rate. In addition, the PSO-BP deep hole blasting fragmentation prediction model predicts the block rate of the optimized blasting parameters and predicted a block rate of 6.83% after the optimization of hole network parameters. Its prediction accuracy is high, and the blasting parameter optimization can effectively reduce the block rate. It can reasonably reduce the rate of large pieces produced by blasting, improve blasting efficiency, and save blasting costs for enterprises. The result has wide applicability and can provide solutions for underground mines that also have problems with blasting large block rate.

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“…The use of explosives to obtain aggregates is crucial from an economic perspective, especially for medium-and high-strength rocks, as well as abrasive materials. In this regard, a lot of research has been focused on the geologic considerations [1,2], geotechnical characteristics [3,4], design of the blast [5] and different types of mining activities [6,7], analyzing the size of the material blasted in some cases [8]. However, there is a lack of information integrating vibrations and other environmental constraints, together with the study of achieving an efficient blast.…”

Section: Introductionmentioning

confidence: 99%

Holistic Approach to Define the Blast Design in Quarrying

Fernández¹,

Rodríguez-Montañés

Bascompta

2022

Minerals

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A new approach to determine the most efficient blast design was proposed, taking the peak particle velocity (PPV) as the main constraint, while including costs, production, granulometry, air blast and carbon footprint as relevant characteristics. The research was based on a case study that registered and analyzed 548 blasts over more than 10 years, while the vibration of the blast was recorded in 32% of them. A general attenuation law and several local laws were determined based on the geological conditions of the quarry. The blast design improvement allowed one to reduce the specific consumption by almost 30%, 26% of the blast cost in terms of explosive consumption and 18% of the CO2 emissions. This allowed for the achievement of a more environmentally friendly extraction system while complying with the legal standards, the quality requirements and the production and productivity conditions established.

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Predicting rock size distribution in mine blasting using various novel soft computing models based on meta-heuristics and machine learning algorithms

Cited by 65 publications

References 64 publications

Advances in Blast-Induced Impact Prediction—A Review of Machine Learning Applications

Advances in Blast-Induced Impact Prediction—A Review of Machine Learning Applications

Parameter Optimization and Fragmentation Prediction of Fan-Shaped Deep Hole Blasting in Sanxin Gold and Copper Mine

Holistic Approach to Define the Blast Design in Quarrying

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