Rock Splitters, Quarry

Today, quarrying uses more machines and data tools, but rock splitting tasks remain mostly hands-on. Big mines use self-driving haul trucks, automated drills and drones for surveying (for example, Rio Tinto’s driverless trucks and AI-guided drill rigs ^{[1] [2]}). Cameras and AI systems are also used to monitor crushers and conveyors for damage or oversize rocks ^{[2] [3]}.

However, the core duties of a rock splitter – spotting natural grain lines in stone, marking cut lines with chalk, and inserting wedges – still rely on a person’s judgment and dexterity ^{[4] [5]}. Industry articles note specialized machines (laser-guided saws, hydraulic splitters) that ease some work, but these require human setup and control ^{[5] [5]}. In short, AI and automation help with planning, sensing and heavy cutting, but no mainstream AI system yet reads a rock’s grain or “lines out” a chalk line on its own.

Quarry job descriptions still list tasks like “locate grain line patterns” and “mark outlines” as core duties ^[4], highlighting how much this work depends on human skill.

Whether quarries adopt more AI quickly or slowly depends on cost, benefit and trust. Large mining firms see clear value: for example, BHP reports that AI analysis can catch equipment failures early and digest huge geological datasets more accurately than people ^{[3] [3]}. Metso, a mining equipment maker, says AI diagnostics can halve downtime by predicting breakdowns ^[6].

These benefits (safety, efficiency and data insights) motivate investment in AI tools. However, implementing AI systems or robots is expensive. Many quarries are small operations where a new drill or splitter machine might cost more than a year of labor.

Since rock splitters’ day-to-day pay is modest (around $22/hr) and they work in variable conditions, companies weigh carefully whether a machine or AI is worth it.