What we modeled: six simulation dimensions
Before any real truck rolled, we built a digital twin of the pit. The simulation is accurate enough to stress-test fleet behaviour, layout constraints and safety rules — and flexible enough to run counter-factual scenarios in hours rather than weeks.
Terrain
Topography, benches and haul-road geometry captured from survey data and imported as the 3D base layer.
Infrastructure
Crushers, stockpiles, workshops, fuel bays and operational structures modeled with real footprints.
Routes & ramps
Up- and down-hill haul paths with realistic slopes, curvature radius and interference corridors.
Fleet dynamics
Autonomous haul-truck vehicle dynamics: loading, hauling, queueing, unloading and return cycles.
Safety rules
Right-of-way, exclusion zones, speed caps and interaction rules where people or equipment might share the road.
Haulage KPIs
Cycle time, throughput, queue length, empty-travel ratio — captured as baseline and re-measured every iteration.
Our six-step methodology
We follow the same disciplined path on every 3D-simulation engagement. The structure is intentionally ordered so each step builds on verified data from the previous one — no step is skipped for speed.
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01
Capture terrain
Capture high-resolution terrain via LiDAR or drone survey and import it as the base 3D model. Ground-truth is non-negotiable — the rest of the simulation inherits any error at this step.
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02
Model infrastructure
Model roads, ramps, crushers, stockpiles and operational infrastructure in the 3D environment at real footprint and correct topological relationships.
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03
Configure fleet
Configure the autonomous haul-truck fleet with vehicle dynamics, loading/unloading cycles and mandatory safety rules — the simulation respects the same constraints the real trucks will.
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04
Run baseline simulation
Run the current haulage plan as a baseline to establish safety posture, throughput and cycle-time KPIs. Without a baseline, every later "improvement" is unverifiable.
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05
Iterate scenarios
Iterate route, ramp and interference scenarios to expose bottlenecks, unsafe interactions and layout weaknesses. Each iteration re-measures the KPI set so trade-offs are visible.
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06
Transfer know-how
Package findings as a structured mine-layout brief so the simulation discipline becomes reusable input for future mine-layout development studies.
"A 3D simulator is not a demo. It is how we buy ourselves the right to be wrong — cheaply and in the virtual, where mistakes cost nothing."
— The Data Riders Operating Model
Documented value
The source material explicitly associates this work with four tangible outcomes:
- Route optimization for autonomous vehicle trajectories up and down the open pit.
- Reinforced safety in critical areas — interactions, blind corners, ramp intersections.
- Material-handling efficiency — better cycle times and reduced empty-travel ratio.
- Know-how transfer — the discipline developed in this simulation work informs subsequent mine-layout development studies.
Know-how built for mine-layout development
The discipline generated by this 3D simulation work — terrain modeling, route dynamics, haul-truck behaviour under operating rules — becomes a reusable asset for subsequent mine-layout development studies.
Why it matters
High-fidelity virtual environments make it possible to stress-test fleet behaviour, layout constraints and safety challenges in a controlled setting — a simulation-first posture that turns layout decisions from opinion into evidence.
Related reads, cases and services
- Case: Multi-criteria tailings alternatives analysis
- Case: AI agent for water balance governance
- Service: AI Solutions
- Service: Technical Services
- Data Riders Agents
- Blog: Quantum computing in mining
- Blog: Lunar mining competitiveness
Editorial note. This case is intentionally conservative. The 3D simulation and autonomous-vehicle work is evidenced by Data Riders' internal case pack and by the publicly available live demonstration on YouTube. Specific performance figures or client-approved screenshots may be added in future updates as they are released for external publication.