Blasting is fundamental to hard rock mining, but it’s also one of the most variable aspects of mining operations. Rock properties change constantly, and the relationship between blast design and outcomes is complex. Artificial intelligence is enabling optimisation that delivers more consistent, better results.

The Blast Optimisation Challenge

Traditional blast design relies on empirical relationships and engineering judgment:

Variable rock conditions: Geology changes within blast patterns. Holes drilled metres apart may encounter significantly different rock.

Complex relationships: Burden, spacing, timing, explosive type, and many other parameters interact in non-linear ways.

Outcome measurement: Fragmentation, throw, vibration, and other outcomes are difficult to measure comprehensively.

Delayed feedback: Results aren’t fully known until after digging, which may occur days after blasting.

Experience dependency: Expert blast engineers develop intuition that’s difficult to codify and transfer.

AI offers methods to address these challenges.

How AI Improves Blasting

Machine learning approaches bring new capabilities to blast optimisation:

Pattern recognition: AI identifies relationships between inputs (geology, design parameters, conditions) and outcomes (fragmentation, vibration) that may not be apparent to humans.

Continuous learning: Models improve as more blasts are analysed. Each blast adds to the training dataset.

Geological integration: AI can incorporate detailed geological data – from drilling, from geophysics, from prior blasts – to predict conditions across patterns.

Multi-objective optimisation: Balancing fragmentation, vibration, cost, and other objectives is handled naturally by optimisation algorithms.

Consistency: AI recommendations are consistent given similar inputs, reducing variability from individual engineer preferences.

Data Requirements

Effective blast AI requires comprehensive data:

Drill data: Measure-while-drilling systems capture rock hardness, fracturing, and other properties at every hole.

Geological models: Block model data provides expected rock types and properties throughout the blast volume.

Design parameters: Complete records of explosive products, quantities, timing, and hole patterns.

Loading verification: Records of what was actually loaded versus what was designed.

Outcome measurement: Fragmentation analysis from cameras or laser scanning, vibration measurements, and dig performance data.

Environmental conditions: Weather at blast time can affect outcomes.

The quality of AI recommendations depends directly on data quality and completeness.

Fragmentation Improvement

Better fragmentation improves downstream processes:

Loader productivity: Well-fragmented muck loads faster and with less equipment damage.

Crusher throughput: Consistent feed size increases crusher productivity and reduces wear.

Processing efficiency: Ultimate recovery can be affected by particle size distribution achieved in blasting.

Oversize reduction: Reducing oversized material eliminates secondary breaking costs and delays.

AI-optimised blast designs consistently achieve tighter fragmentation distributions with less oversized material.

Environmental Benefits

AI blast optimisation also delivers environmental improvements:

Vibration reduction: Optimised timing and charge weight distribution minimises ground vibration for given fragmentation outcomes.

Flyrock prevention: AI identifies patterns associated with flyrock risk and recommends protective measures.

Dust management: Better fragmentation and throw control reduces dust generation during blasting.

Noise control: Timing optimisation can reduce air overpressure that causes noise impacts.

Meeting environmental constraints while achieving production objectives is exactly the multi-objective optimisation that AI handles well.

Safety Applications

Blasting safety benefits from AI analysis:

Risk identification: AI can identify blast designs with elevated risk based on patterns in historical incidents.

Exclusion zone optimisation: More accurate prediction of throw and vibration enables right-sized exclusion zones – neither too large nor too small.

Misfire prediction: Patterns in logging data may predict reliability issues before they result in misfires.

Post-blast analysis: AI assists investigation of any blast that produces unexpected outcomes.

Implementation Approaches

Several approaches exist for deploying blast AI:

Vendor solutions: Explosive companies and specialised vendors offer AI-enhanced blast design tools.

Custom development: Operations with unique requirements may develop bespoke systems. Firms offering custom AI development can build systems tailored to specific geological and operational contexts.

Hybrid approaches: Combining vendor platforms with custom enhancements often provides the best of both worlds.

Embedded expertise: Some operations have hired data scientists who work alongside blast engineers to develop and maintain AI systems.

Change Management

Adopting AI blast optimisation requires change management:

Engineer engagement: Blast engineers must be partners in AI development, not recipients of imposed systems. Their expertise is essential.

Validation: AI recommendations should be validated before full adoption. Running AI and conventional approaches in parallel builds confidence.

Override capability: Engineers must be able to override AI when their judgment differs. AI is a tool, not a replacement.

Continuous feedback: Mechanisms for engineers to flag poor recommendations improve system performance over time.

Training: Understanding what AI does and its limitations enables effective use.

Economic Value

Blast optimisation AI delivers measurable value:

Explosive savings: Better matching of explosive energy to rock conditions reduces product consumption.

Processing gains: Improved fragmentation reduces crushing and grinding energy requirements.

Productivity improvement: Better muck conditions increase load and haul productivity.

Vibration compliance: Avoiding vibration limit exceedances prevents costly operational restrictions.

Oversize elimination: Reducing secondary breaking activity saves cost and improves cycle times.

Future Directions

Blast AI will continue to advance:

Real-time adaptation: Future systems may adjust blast designs in real-time based on drilling data from each hole.

Simulation integration: Coupling AI optimisation with physics-based simulation enables testing before execution.

Autonomous integration: Autonomous drilling provides more consistent, detailed data to feed AI systems.

Sensor advances: Emerging sensing technologies will provide better outcome measurement to improve learning.

Broad deployment: As AI tools mature and costs decrease, adoption will extend to smaller operations.

Blasting remains as much art as science at many operations. AI is shifting this balance toward science without eliminating the need for engineering judgment. The result is more consistent, better-optimised blasts that improve safety, efficiency, and environmental performance.