journal article·2025-10-18Open access

Research on an Adaptive Hole Layout Method for Bench Blasting Based on Voronoi Diagram

Maolin He, Xiaojun Zhang, Xiaoshuai Li, Wenxue Gao

Applied Sciences· Volume 15· Issue 20

Abstract

In open-pit bench blasting design, conventional hole placement methods are limited by their inability to handle irregular blast area boundaries effectively. To address this, an adaptive hole placement algorithm based on Voronoi diagrams is proposed. This algorithm uses Voronoi diagram principles to divide the blast area according to its boundary conditions. Using Lloyd’s algorithm achieves a uniform distribution of blast hole points within the blast zone, enabling the p3rediction of hole coordinates. The algorithm has been developed into a bench blasting design programme using MATLAB R2021a. The programme calculates the required number of blast holes based on coverage area per blast hole charge and blast area. It then completes the entire bench blasting design by incorporating parameters such as the blast area boundary. In practice, this method enables more scientific blast design, demonstrating excellent algorithm stability and computational efficiency. It is particularly adaptable when handling irregular blast area boundaries.
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Research context

OpenAlex topics: Image Processing and 3D Reconstruction · Manufacturing Process and Optimization · Simulation and Modeling Applications

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

School of Architecture and Engineering, Beijing University of Technology, Beijing 100124, China

Hubei Key Laboratory of Blasting Engineering, Jianghan University, Wuhan 430056, China

Beijing Beikuang Yibo Technology Co., Ltd., Mining and Metallurgy Technology Group Co., Ltd., Beijing 100160, China

Funding

Beijing Postdoctoral Research Foundation · Chaoyang District Postdoctoral Work Foundation · Foundation of Hubei Key Laboratory of Blasting Engineering

References (30)

Show source-provided references
1. State-of-the-art review of machine learning and optimization algorithms applications in environmental effects of blasting10.1007/s10462-023-10636-8 ↗
2. Review of machine learning application in mine blasting10.1007/s12517-023-11237-z ↗
3. Fuzzy set approaches to classification of rock masses10.1016/j.enggeo.2004.03.011 ↗
4. Integrating unascertained measurement and information entropy theory to assess blastability of rock mass10.1007/s11771-012-1231-y ↗
5. Intelligent decision system for vertical shaft blasting scheme based on knowledge base and its application10.1109/ACCESS.2021.3128550 ↗
6. Optimization of blasting parameters and prediction of vibration effects in open pit mines based on deep neural networks10.1016/j.aej.2023.02.043 ↗
7. A fundamental study on the automation of tunnel blasting design using a machine learning model
8. Intelligent Optimization of Blasting Parameters in Railroad Tunnels Based on Blasting Quality Control10.1061/JCEMD4.COENG-15907 ↗
9. Decision making with the analytic hierarchy process
10. Evaluation of effect of blasting pattern parameters on back break using neural networks10.1016/j.ijrmms.2008.02.007 ↗
11. Optimization of blasting design in open pit limestone mines with the aim of reducing ground vibration using robust techniques10.1007/s40948-020-00164-y ↗
12. Use of intelligent methods to design effective pattern parameters of mine blasting to minimize flyrock distance10.1007/s11053-019-09519-z ↗
13. Selection of blasting design parameters affecting peak particle velocity—A case study
14. Numerical modelling of destress blasting–a state-of-the-art review10.46873/2300-3960.1366 ↗
15. Study on the effects of blast damage factor and blast design parameters on the ground vibration using 3D discrete element method10.1007/s41062-020-0286-0 ↗
16. Optimization of blast design parameter for ring blasting in underground hard rock mine using numerical simulation
17. Intelligent Design for Mechanized Drilling and Blasting Tunnels Based on Massive Numerical Simulations and Machine Learning Algorithms
18. Study on parameter optimization of deep hole cumulative blasting in low permeability coal seams10.1038/s41598-022-09219-4 ↗
19. Blast parameter optimization study based on a blast crater experiment10.1155/2018/8031735 ↗
20. Study on influencing factors and prediction of peak particle velocity induced by roof pre-split blasting in underground10.1016/j.undsp.2022.02.002 ↗
21. Optimized blasting design for large-scale quarrying based on a 3-D spatial distribution of rock factor10.1016/j.ijrmms.2008.07.006 ↗
22. Effect of barrier holes on blast induced vibration10.1016/j.ijrmms.2007.08.008 ↗
23. Multirobot unknown environment exploration and obstacle avoidance based on a Voronoi diagram and reinforcement learning10.1016/j.eswa.2024.125900 ↗
24. Maruani, N., Klokov, R., Ovsjanikov, M., Alliez, P., and Desbrun, M. (2023, January 2–6). Voromesh: Learning watertight surface meshes with voronoi diagrams. Proceedings of the IEEE/CVF International Conference on Computer Vision, Paris, France.10.1109/ICCV51070.2023.01339 ↗
25. An effective and efficient algorithm for K-means clustering with new formulation10.1109/TKDE.2022.3155450 ↗
26. Stochastic computation of barycentric coordinates
27. Design and optimization for bench blast based on Voronoi diagram10.1016/j.ijrmms.2013.11.012 ↗
28. Monitoring the coefficient of variation: A literature review10.1016/j.cie.2021.107600 ↗
29. Voronoi tessellation to study the numerical density and the spatial distribution of neurones10.1016/S0891-0618(00)00064-8 ↗
30. Wu, C., Yuan, Y., Tang, Y., and Tian, B. (2021). Application of terrestrial laser scanning (TLS) in the architecture, engineering and construction (AEC) industry. Sensors, 22.10.3390/s22010265 ↗