Prediction of blasting mean fragment size using support vector regression combined with five optimization algorithms

Li, Enming; Yang, Fenghao; Ren, Meiheng; Zhang, Xiliang; Zhou, Jian; Khandelwal, Manoj

doi:10.1016/j.jrmge.2021.07.013

Cited by 85 publications

(13 citation statements)

References 82 publications

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“…As shown in Figure 1 , α is the optimal gray wolf, which is the leader of the wolf group and has the right to decide all major issues of the entire wolf group; β is the second-best gray wolf, assisting the leader wolf to make decisions; δ is at the third level, responsible for sentry, reconnaissance and other tasks; ω is the lowest level wolf and is under the command of the first three levels of gray wolves in action. More descriptions of the principle of GWO can be found in the literature ( 56 , 61 ).…”

Section: Methodsmentioning

confidence: 99%

“…In addition, in order to train and test the model, the database is randomly divided into a training set and a test set, and their data distribution status is represented by a violin plot, as shown in Figure 4 . The violin plot is a combination of a box plot and a kernel density plot, where the black boxes indicate the range from the lower quartile to the upper quartile and the red dots in the middle represent the mean ( 61 ). From the figure, it can be seen that the parameter data distributions of the training set and testing set are approximately the same in order to ensure that the results obtained in this study will be reliable.…”

Section: Methodsmentioning

confidence: 99%

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Hybridizing five neural-metaheuristic paradigms to predict the pillar stress in bord and pillar method

Zhou

Chen

et al. 2023

Front. Public Health

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Pillar stability is an important condition for safe work in room-and-pillar mines. The instability of pillars will lead to large-scale collapse hazards, and the accurate estimation of induced stresses at different positions in the pillar is helpful for pillar design and guaranteeing pillar stability. There are many modeling methods to design pillars and evaluate their stability, including empirical and numerical method. However, empirical methods are difficult to be applied to places other than the original environmental characteristics, and numerical methods often simplify the boundary conditions and material properties, which cannot guarantee the stability of the design. Currently, machine learning (ML) algorithms have been successfully applied to pillar stability assessment with higher accuracy. Thus, the study adopted a back-propagation neural network (BPNN) and five elements including the sparrow search algorithm (SSA), gray wolf optimizer (GWO), butterfly optimization algorithm (BOA), tunicate swarm algorithm (TSA), and multi-verse optimizer (MVO). Combining metaheuristic algorithms, five hybrid models were developed to predict the induced stress within the pillar. The weight and threshold of the BPNN model are optimized by metaheuristic algorithms, in which the mean absolute error (MAE) is utilized as the fitness function. A database containing 149 data samples was established, where the input variables were the angle of goafline (A), depth of the working coal seam (H), specific gravity (G), distance of the point from the center of the pillar (C), and distance of the point from goafline (D), and the output variable was the induced stress. Furthermore, the predictive performance of the proposed model is evaluated by five metrics, namely coefficient of determination (R2), root mean squared error (RMSE), variance accounted for (VAF), mean absolute error (MAE), and mean absolute percentage error (MAPE). The results showed that the five hybrid models developed have good prediction performance, especially the GWO-BPNN model performed the best (Training set: R2 = 0.9991, RMSE = 0.1535, VAF = 99.91, MAE = 0.0884, MAPE = 0.6107; Test set: R2 = 0.9983, RMSE = 0.1783, VAF = 99.83, MAE = 0.1230, MAPE = 0.9253).

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Hybridizing five neural-metaheuristic paradigms to predict the pillar stress in bord and pillar method

Zhou

Chen

et al. 2023

Front. Public Health

Self Cite

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“…A plane with the farthest distance from the point on the boundary to the plane is found by classification, and of an insensitive loss function as the loss function is introduced in the SVR to minimize the distance from each point to the regression line, which is used for controlling the distance between the actual values and boundary values, and loss function is used for determining if the value of is located in the range of y ε, then the calculation loss can be ignored 38 , 39 …”

Section: Optimal Model Of Filter Press Dehydration Process In Laboratorymentioning

confidence: 99%

Optimization of dewatering process of concentrate pressure filtering by support vector regression

Liu

You

2022

Sci Rep

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This work studies the mechanism and optimization methods of the filter press dehydration process to better improve the efficiency of the concentrate filter press dehydration operation. Machine learning (ML) models of radial basis function (RBF)–OLS, RBF-generalized regression neural network, and support vector regression (SVR) are constructed, and laboratory and industrial simulations are performed separately, finally, optimization methods for the filtration dewatering process are designed and applied. In laboratory, all the machine learning models have obvious mistakes, but it can be seen that SVR has the best simulation effect. In order to achieve the optimization of the entire filtration and dewatering process, we obtained enough data from the industrial filtration and dewatering system, and in the industrial simulation results all the machine learning models performed considerably, SVR achieves the best accuracy in industrial simulation, and the simulated mean relative error of moisture and processing capacity are 1.57% and 3.81%, the model was tested with newly collected industrial data to verify the credibility. The optimal simulation results are obtained by optimization method based on control variables. Results show that the ML method of SVR and optimization methods of control variables applied to the industry not only can save energy consumption and cost but also can improves the efficiency of filter press operation fundamentally, which will provide some options for intelligent dewatering process and other industrial production optimization.

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“…Under the condition of the uniaxial load, the shape of the crater in the long axial loading direction was elliptical with the increase in the confining pressure, and the opening angle and pit volume in this direction increased (Peng et al, 2020;Zhang et al, 2021). Some experiments and numerical simulation studies were carried out on rock blasting under high ground stress (Li et al, 2021;Jayasinghe et al, 2019). The research results show that the existence of high ground stress changed the propagation direction and propagation length of explosion-induced cracks.…”

Section: Introductionmentioning

confidence: 99%

“…The aforementioned studies illustrated the influence of static stress on the blasting fracture of rock materials, and in situ stress played a key role in crack initiation and growth. Based on these studies, the influence of in situ stress on blasting rock fracture was analyzed and experimentally studied (Li et al, 2021;Wu et al, 2015;Zhang et al, 2017). The results show that the cracks caused by blasting stress wave and gas pressure grow well in the direction of the maximum principal stress of the superimposed stress field (Kutter and Fairhurst, 1971).…”

Section: Introductionmentioning

confidence: 99%

Experimental Study on Fracturing Characteristics of Double-Hole Blasting Under Static Stresses

et al. 2022

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This study aims to investigate the fracturing characteristics of double-hole blasting under the action of coupled static stress and delayed time explosion. A total of thirteen explosive tests were carried out on rectangular concrete blocks with different constraints. The test blocks were wrapped in steel mesh in the test bed to prevent flying away of the cracked blocks after blasting. After blasting, the surface area of the crater was measured, and all pieces of the cracked blocks were collected, screened, and analyzed. The experimental results show that (1) the shape of free-surface craters was affected by static stress. The surface area and overall volume of the craters increased in the direction of loading stress. (2) The crater under simultaneous initiation was larger than that under delayed initiation when the tested block had a single free surface. (3) The fragmentation of blasting is closely related to the static confining pressure and delay initiation applied on the tested specimens. It is believed that this research will contribute to the design of multi-hole blasting in the deep geological body in the respect of the theoretical analysis.

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Prediction of blasting mean fragment size using support vector regression combined with five optimization algorithms

Cited by 85 publications

References 82 publications

Hybridizing five neural-metaheuristic paradigms to predict the pillar stress in bord and pillar method

Hybridizing five neural-metaheuristic paradigms to predict the pillar stress in bord and pillar method

Optimization of dewatering process of concentrate pressure filtering by support vector regression

Experimental Study on Fracturing Characteristics of Double-Hole Blasting Under Static Stresses

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