An explainable AI-based framework for predicting and optimizing blast-induced ground vibrations in surface mining Article Swipe
Charan Kumar
,
Zefree Lazarus Mayaluri
,
Amit Kaushik
,
Nikhat Parveen
,
Surabhi Saxena
,
Abu Taha Zamani
,
Debendra Muduli
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.rineng.2025.106046
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.rineng.2025.106046
Blast induced ground vibrations (BIGV) pose critical challenges in surface mining, threatening structural integrity, worker safety, and environmental compliance. This study proposes a novel hybrid artificial intelligence (AI) framework that integrates physics informed neural networks (PINNs) with conventional machine learning (ML) algorithms for the accurate prediction and optimization of BIGV. Unlike empirical equations that lack generalizability or black box ML models with limited transparency, the proposed approach embeds domain specific physical laws while leveraging data driven learning to improve both predictive accuracy and interpretability. A multiobjective optimization scheme is employed to balance competing goals: minimizing peak particle velocity (PPV), maximizing fragmentation efficiency, and reducing operational costs. Crucially, the framework incorporates Explainable AI (XAI) techniques such as Shapley Additive Explanations (SHAP) and Local Interpretable Model Agnostic Explanations (LIME) and uncertainty quantification (UQ) methods based on Bayesian Neural Networks to provide insight into model decisions and confidence in predictions. Validation across five operational mines in the Godavari Valley Coalfields (India) demonstrates strong generalizability, achieving up to a 20% reduction in RMSE compared to empirical baselines. The improvement is statistically significant (p<0.01) as confirmed through a paired t-test across cross-validation folds. These findings highlight that a physics informed, explainable, and uncertainty aware AI framework can substantially improve vibration prediction, ensure regulatory compliance, and support safer, more sustainable blasting operations in modern surface mining.
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- OA Status
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https://openalex.org/W4412487599Canonical identifier for this work in OpenAlex
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https://doi.org/10.1016/j.rineng.2025.106046Digital Object Identifier
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An explainable AI-based framework for predicting and optimizing blast-induced ground vibrations in surface miningWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-07-08Full publication date if available
- Authors
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Charan Kumar, Zefree Lazarus Mayaluri, Amit Kaushik, Nikhat Parveen, Surabhi Saxena, Abu Taha Zamani, Debendra MuduliList of authors in order
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https://doi.org/10.1016/j.rineng.2025.106046Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1016/j.rineng.2025.106046Direct OA link when available
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Surface (topology), Ground vibrations, Vibration, Computer science, Geology, Physics, Mathematics, Acoustics, GeometryTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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| abstract_inverted_index.induced | 1 |
| abstract_inverted_index.insight | 139 |
| abstract_inverted_index.limited | 62 |
| abstract_inverted_index.machine | 38 |
| abstract_inverted_index.methods | 131 |
| abstract_inverted_index.mining, | 10 |
| abstract_inverted_index.mining. | 219 |
| abstract_inverted_index.physics | 31, 193 |
| abstract_inverted_index.provide | 138 |
| abstract_inverted_index.safety, | 15 |
| abstract_inverted_index.support | 210 |
| abstract_inverted_index.surface | 9, 218 |
| abstract_inverted_index.through | 181 |
| abstract_inverted_index.(p<0.01) | 178 |
| abstract_inverted_index.Additive | 117 |
| abstract_inverted_index.Agnostic | 124 |
| abstract_inverted_index.Bayesian | 134 |
| abstract_inverted_index.Godavari | 154 |
| abstract_inverted_index.Networks | 136 |
| abstract_inverted_index.accuracy | 81 |
| abstract_inverted_index.accurate | 44 |
| abstract_inverted_index.approach | 66 |
| abstract_inverted_index.blasting | 214 |
| abstract_inverted_index.compared | 169 |
| abstract_inverted_index.critical | 6 |
| abstract_inverted_index.employed | 89 |
| abstract_inverted_index.findings | 189 |
| abstract_inverted_index.informed | 32 |
| abstract_inverted_index.learning | 39, 76 |
| abstract_inverted_index.networks | 34 |
| abstract_inverted_index.particle | 96 |
| abstract_inverted_index.physical | 70 |
| abstract_inverted_index.proposed | 65 |
| abstract_inverted_index.proposes | 21 |
| abstract_inverted_index.reducing | 103 |
| abstract_inverted_index.specific | 69 |
| abstract_inverted_index.velocity | 97 |
| abstract_inverted_index.achieving | 161 |
| abstract_inverted_index.competing | 92 |
| abstract_inverted_index.confirmed | 180 |
| abstract_inverted_index.decisions | 142 |
| abstract_inverted_index.empirical | 51, 171 |
| abstract_inverted_index.equations | 52 |
| abstract_inverted_index.framework | 28, 108, 200 |
| abstract_inverted_index.highlight | 190 |
| abstract_inverted_index.informed, | 194 |
| abstract_inverted_index.reduction | 166 |
| abstract_inverted_index.vibration | 204 |
| abstract_inverted_index.Coalfields | 156 |
| abstract_inverted_index.Crucially, | 106 |
| abstract_inverted_index.Validation | 147 |
| abstract_inverted_index.algorithms | 41 |
| abstract_inverted_index.artificial | 25 |
| abstract_inverted_index.baselines. | 172 |
| abstract_inverted_index.challenges | 7 |
| abstract_inverted_index.confidence | 144 |
| abstract_inverted_index.integrates | 30 |
| abstract_inverted_index.integrity, | 13 |
| abstract_inverted_index.leveraging | 73 |
| abstract_inverted_index.maximizing | 99 |
| abstract_inverted_index.minimizing | 94 |
| abstract_inverted_index.operations | 215 |
| abstract_inverted_index.prediction | 45 |
| abstract_inverted_index.predictive | 80 |
| abstract_inverted_index.regulatory | 207 |
| abstract_inverted_index.structural | 12 |
| abstract_inverted_index.techniques | 113 |
| abstract_inverted_index.vibrations | 3 |
| abstract_inverted_index.Explainable | 110 |
| abstract_inverted_index.compliance, | 208 |
| abstract_inverted_index.compliance. | 18 |
| abstract_inverted_index.efficiency, | 101 |
| abstract_inverted_index.improvement | 174 |
| abstract_inverted_index.operational | 104, 150 |
| abstract_inverted_index.prediction, | 205 |
| abstract_inverted_index.significant | 177 |
| abstract_inverted_index.sustainable | 213 |
| abstract_inverted_index.threatening | 11 |
| abstract_inverted_index.uncertainty | 128, 197 |
| abstract_inverted_index.Explanations | 118, 125 |
| abstract_inverted_index.conventional | 37 |
| abstract_inverted_index.demonstrates | 158 |
| abstract_inverted_index.explainable, | 195 |
| abstract_inverted_index.incorporates | 109 |
| abstract_inverted_index.intelligence | 26 |
| abstract_inverted_index.optimization | 47, 86 |
| abstract_inverted_index.predictions. | 146 |
| abstract_inverted_index.Interpretable | 122 |
| abstract_inverted_index.environmental | 17 |
| abstract_inverted_index.fragmentation | 100 |
| abstract_inverted_index.statistically | 176 |
| abstract_inverted_index.substantially | 202 |
| abstract_inverted_index.transparency, | 63 |
| abstract_inverted_index.multiobjective | 85 |
| abstract_inverted_index.quantification | 129 |
| abstract_inverted_index.cross-validation | 186 |
| abstract_inverted_index.generalizability | 55 |
| abstract_inverted_index.generalizability, | 160 |
| abstract_inverted_index.interpretability. | 83 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/13 |
| sustainable_development_goals[0].score | 0.5799999833106995 |
| sustainable_development_goals[0].display_name | Climate action |
| citation_normalized_percentile.value | 0.34264107 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |