Prediction of flyrock trajectories for forensic applications using ballistic flight equations

Stojadinović, Saša; Pantović, Radoje; Žikić, Miodrag

doi:10.1016/j.ijrmms.2011.07.004

Cited by 31 publications

(6 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a result, researchers have made efforts to develop empirical models to predict and help mitigate flyrock. Equations have also been formulated based on Newton's law of motion with two possible solutions: an approximate numerical solution and the application of the Runge-Kutta algorithm of the fourth order to predict the maximum throw of flyrock fragments and estimate safe distances [139]. More recently, ML has proven to be a useful tool with surging applications in predicting flyrock.…”

Section: Flyrockmentioning

confidence: 99%

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

2021

View full text Add to dashboard Cite

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.

show abstract

Section: Flyrockmentioning

confidence: 99%

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

2021

View full text Add to dashboard Cite

show abstract

“…Flyrock is unwanted rock fragments thrown during bench blasting in mines and civil constructions [ 5 ]. Flyrock, which is propelled rock fragments by energy of explosive beyond the blast zone, is one of the undesirable environmental impacts of blasting operations [ 25 ]. In flyrock mechanism, there is an affective relationship among explosive energy distribution, rock mass mechanical strength, and charge confinement.…”

Section: Flyrock and Effective Parametersmentioning

confidence: 99%

A Novel Approach for Blast-Induced Flyrock Prediction Based on Imperialist Competitive Algorithm and Artificial Neural Network

Marto

Hajihassani

Armaghani

et al. 2014

The Scientific World Journal

112

View full text Add to dashboard Cite

Flyrock is one of the major disturbances induced by blasting which may cause severe damage to nearby structures. This phenomenon has to be precisely predicted and subsequently controlled through the changing in the blast design to minimize potential risk of blasting. The scope of this study is to predict flyrock induced by blasting through a novel approach based on the combination of imperialist competitive algorithm (ICA) and artificial neural network (ANN). For this purpose, the parameters of 113 blasting operations were accurately recorded and flyrock distances were measured for each operation. By applying the sensitivity analysis, maximum charge per delay and powder factor were determined as the most influential parameters on flyrock. In the light of this analysis, two new empirical predictors were developed to predict flyrock distance. For a comparison purpose, a predeveloped backpropagation (BP) ANN was developed and the results were compared with those of the proposed ICA-ANN model and empirical predictors. The results clearly showed the superiority of the proposed ICA-ANN model in comparison with the proposed BP-ANN model and empirical approaches.

show abstract

“…A particularly troublesome aspect of wild flyrock is that large fragments can be thrown unexpected distances. For example, Stojadinovic, Pantovic, and Zikic (2011) noted a sizeable rock punching a hole through the brick wall of a double-storey residence located 360 m from the bench. Lundborg (1981) published a photograph of a 3 t boulder that landed 300 m from and 40 m above its source.…”

Section: Introductionmentioning

confidence: 99%

Flyrock in surface mining - Limitations of current predictive models and a better alterative through modelling the aerodynamics of flyrock trajectory

Szendrei¹,

Tose

2023

J. S. Afr. Inst. Min. Metall.

View full text Add to dashboard Cite

Historical approaches to the problem of flyrock based on correlation studies and regression analysis, including artificial neural networks and similar techniques, are inherently incapable of addressing two core issues - root causes of flyrock and projection velocity. A further shortcoming of correlation techniques is that they give no information on the influence of rock size and shape on the flight distance. The scaled depth of burial model for crater blasting in the collar zone and bench face does not specifically address the question of flyrock velocity. A third approach, based on flight trajectory calculations, often neglects the very significant effects of air resistance on the trajectory. Some trajectory models incorporate air resistance but use an implausible fragment velocity model that cannot propel sizeable rocks to distances much beyond 150 m. Nonetheless, trajectory calculation incorporating the effects of air drag affords the most promising approach to the prediction of flyrock range. A unique and insightful feature of the proposed realistic flight modelling is that it collapses all suspected causes of flyrock, many of which are not well understood, to just a single parameter - the launch velocity. This indicates that the root causes of flyrock lie in the mechanisms of momentum transfer to broken rock and suggests new avenues of study.

show abstract

Prediction of flyrock trajectories for forensic applications using ballistic flight equations

Cited by 31 publications

References 2 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

A Novel Approach for Blast-Induced Flyrock Prediction Based on Imperialist Competitive Algorithm and Artificial Neural Network

Flyrock in surface mining - Limitations of current predictive models and a better alterative through modelling the aerodynamics of flyrock trajectory

Contact Info

Product

Resources

About