How Haul Road Roughness and Rolling Resistance Affect Cycle Time and Tyre Wear
If trucks are slow and tyres are dying early, start with the road.
On a mine haul network, roughness, soft spots, ruts and debris do not stay "road problems." They show up as longer cycles, higher fuel burn, speed derates and premature OTR wear. Mine services, production engineers and tech services already feel that in the weekly pack. What they often lack is a clear view of which sections are creating the cost.
This guide connects haul road condition to rolling resistance, cycle time and tyre wear in practical language, then shows how objective condition data (heat maps and Road Score) helps you prioritise maintenance. Monitoring framing only: not a construction method guide, not a dust or stabilisation pitch, and not a tyre product page.
The ops reality: road condition becomes fleet cost
A rough or soft corridor forces the truck to work harder for the same payload. Drivers lift off, brake and re-accelerate. Average speed falls even when peak speed still looks fine on a good stretch. Tyres take more impact, scrub and heat.
Multi-OEM tyre specialists say the same thing from the rubber side: poorly designed or poorly maintained haul roads drive cuts, impacts, uneven wear and heat that shorten OTR life (For Construction Pros, quoting Michelin Earthmover, Bridgestone Off Road and Goodyear OTR voices).
That is why "tyre quality" arguments can run for months while the same three soft spots keep beating up the fleet.
Rolling resistance, in plain English
Rolling resistance is the force that must be overcome to roll a tyre over the ground. Soft, rutted, uneven or poorly maintained surfaces raise it, because energy goes into penetration, flexing and bounce instead of moving tonnes.
Two planning rules of thumb from public industry sources:
- Haul Road Design Resources (Thompson-associated): a 1% increase in rolling resistance can typically reduce speed on ramp by as much as 10% and on the flat by up to 26% (haul-road-design.com).
- Hexagon Mining: every 1% increase in rolling resistance effectively adds the same load as a 1% increase in road gradient, so small road-quality changes matter for fuel burn; operational monitoring of road condition sits alongside design and maintenance (Hexagon blog).
A peer-reviewed iron ore mine study (Silva et al., open access on SciELO) links managing grade and rolling resistance to lower truck cycle time and fuel use and higher per-truck productivity in that operation, and connects rough/irregular roads to reduced tire durability (Silva et al.). Those figures are that paper's case results, not Proof Engineers outcomes.
Treat the industry rules of thumb and the SciELO case as context for why condition priority is an economics problem. Do not read them as PE product guarantees.
Proof Engineers Road Condition Monitoring (RCM) monitors haul road condition with vehicle-mounted sensors, GPS and cloud analytics. Teams use road condition heat maps and Road Score to compare sections and prioritise maintenance. RCM does not measure rolling resistance % and is not marketed as an RR% meter or RR KPI. The product job is clearer: show which roads are degrading so maintenance hits the sections that are taxing speed and rubber.
How roughness and defects steal cycle time
Different defects hit the cycle in different ways:
- Corrugations create a brake-and-accelerate rhythm that destroys average speed.
- Ruts and soft spots raise the energy cost of every metre and push operators into slower gears or alternate lines.
- Potholes, rocks and spillage add impact events and deliberate speed cuts for safety.
- Ponding / over-wet surfaces leave soft running that feels like dragging payload you did not plan for.
Sites answer with speed derates. That protects people. It also locks longer cycles into the plan until the section is fixed. Hexagon's point lands here: design intent is not enough if operating road condition is not monitored (Hexagon).
Why tyre wear follows the same map
OTR tyres fail for many reasons. Road condition is one you can manage.
Repeated impacts over undulations and debris put heat and structural stress into the casing. Soft running increases scrub. Spillage and rock create cut risk. Over a month of shifts, those "small" road events compound.
That is exactly the OTR maintenance story in the For Construction Pros round-up: haul road design and maintenance show up as cuts, impacts, heat and wear patterns that shorten tyre life (For Construction Pros). The SciELO case likewise treats rough roads as a tire-durability factor in its literature framing (Silva et al.).
For a road maintenance superintendent, the useful question is: which named sections sit on both the slow-truck list and the tyre-damage list?
Gut feel is not a network maintenance system
Every site knows its famous bad road. The expensive gaps are the ones that only fail after weather, only in one lane, or only when traffic shifts.
Walkovers and bump-style checks still matter for safety, handovers and specific risks. They are a weak sole system for network priority, because coverage depends on who drove what and when. Continuous condition data complements inspections; it does not replace site judgement.
More detail: Road Condition Monitoring vs Manual Inspections
What objective condition data changes
With RCM, Proof Engineers provides standalone monitoring for mine haul roads. Vehicle-mounted sensors collect condition data as fleet vehicles travel the network. Data is uploaded to the cloud for live visibility, so teams can review condition shift-to-shift and across maintenance cycles (not a one-off walkover snapshot).
Teams typically work with:
- Heat maps of road condition across the network
- Road Score - a summary / benchmark metric in Proof Engineers' RCM (not a universal industry standard) - to compare sections and watch trends after grading or weather
- Section-level views that support a weekly maintenance queue
Road Score sits alongside heat maps and section-level data. Use it to set baselines, track trends across shifts and maintenance cycles, and support maintenance conversations. It is not rolling resistance %, IRI, or a tyre KPI.
How condition data feeds planning: How Road Condition Data Improves Haul Road Maintenance Planning
That is the bridge from economics to action: you still make the call, but you make it from section-level evidence.
Field evidence from Proof Engineers case studies
Public Proof Engineers case studies report truck speed and Road Score movement after targeted maintenance guided by road condition monitoring. Examples on the site include a Western Australia lithium trial and a dig-floor maintenance study. Those results are about condition-led maintenance and haul performance, not a claim that RCM measured rolling resistance percentage.
Published figures live with the case studies: Resources and case studies
Once priorities are clear, check the grader work landed
Knowing the worst sections is half the job. The other half is whether grading effort hit them.
Grader Performance Monitoring (GPM) distinguishes productive / working grader activity from travelling and idle, including work intensity views on the product page. After an RCM-led priority list, the practical check is simple: did graders work the sections at the top of the queue?
What good looks like this week
- Name the sections constraining loaded or empty speed right now.
- Mark which of those also sit on tyre damage, heat or debris risk.
- Separate mandatory walkover territory from the data-led queue.
- After grading, confirm condition improved on those sections, not only that hours were spent.
- Kill one habit job that exists only because "we always grade that road."
If (1) and (4) still start an argument, you do not have enough shared condition visibility yet.
Next step
If you want a clearer map of which haul road sections are driving slow cycles and rough running, start with RCM and Road Score:
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