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<p>We present a design-ready, multi-output GA-ANN framework for simultaneous prediction and design optimisation of blast impacts, rock fragmentation, ground vibration, and airblast at Debswana’s Jwaneng Mine. Using ten input parameters from 120 production blasts, the model both predicts these impacts and supports rapid, constraint-aware blast design that meets regulatory limits while improving productivity. We compare genetic algorithm-artificial neural network (GA-ANN) with particle swarm optimisation (PSO-ANN), artificial bee colony (ABC-ANN), and imperialist competitive algorithm (ICA-ANN), select GA-ANN on held-out accuracy, construct a multi-output solution surface (10-70-25-3 architecture via Monte Carlo), and apply gradient-descent inverse design. On the test set, GA-ANN attains R2 = 0.910 (fragmentation), 0.925 (ground vibration), and 0.967 (airblast); inverse design returns input settings that maximise fragmentation (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\approx \)</EquationSource> </InlineEquation> 84%) while minimising vibration (<InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\approx \)</EquationSource> </InlineEquation> 0.10 mm/s) and airblast (<InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(\approx \)</EquationSource> </InlineEquation> 41 dB). The learned solution surface reveals operational trade-offs, enables fast constraint-aware “what-if” queries, and provides a retrainable path to safer, more efficient, compliance-oriented blast design at scale.</p>

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Simultaneous prediction and design optimisation of blast impacts at Jwaneng mine using a GA ANN framework

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月24日

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  • Onalethata Saubi,
  • Rodrigo S. Jamisola, Jr.,
  • Raymond S. Suglo,
  • Oduetse Matsebe

摘要

We present a design-ready, multi-output GA-ANN framework for simultaneous prediction and design optimisation of blast impacts, rock fragmentation, ground vibration, and airblast at Debswana’s Jwaneng Mine. Using ten input parameters from 120 production blasts, the model both predicts these impacts and supports rapid, constraint-aware blast design that meets regulatory limits while improving productivity. We compare genetic algorithm-artificial neural network (GA-ANN) with particle swarm optimisation (PSO-ANN), artificial bee colony (ABC-ANN), and imperialist competitive algorithm (ICA-ANN), select GA-ANN on held-out accuracy, construct a multi-output solution surface (10-70-25-3 architecture via Monte Carlo), and apply gradient-descent inverse design. On the test set, GA-ANN attains R2 = 0.910 (fragmentation), 0.925 (ground vibration), and 0.967 (airblast); inverse design returns input settings that maximise fragmentation ( \(\approx \) 84%) while minimising vibration ( \(\approx \) 0.10 mm/s) and airblast ( \(\approx \) 41 dB). The learned solution surface reveals operational trade-offs, enables fast constraint-aware “what-if” queries, and provides a retrainable path to safer, more efficient, compliance-oriented blast design at scale.