ROI Calculator

Calculate your potential return on investment from soil stabilization. Select your industry and input your operational parameters to see projected savings.

Select an industry to begin:

Click on one of the buttons below to access the calculator

Sources & Methodology

This calculator uses industry-validated benchmarks from published engineering research, OEM specifications, and independently verified mine site case studies. All defaults are conservative — engineers are encouraged to replace them with site-specific data from CMMS/ERP systems.

Disclaimer: All cost benchmarks are industry averages. Local fuel prices, labor rates, and maintenance contracts will differ significantly by region and operation. Use site-specific data (CMMS/ERP actuals) for final capital investment decisions.

Rolling Resistance · Defaults

Tannant & Regensburg (2001)

Guidelines for Mine Haul Road Design. University of British Columbia. Provides the foundational rolling resistance surface type table used as default RR values: 4–6% unpaved gravel, 2.7–3.5% with stabilization treatment.

Source for: RR baseline & improved defaults

Truck Specs · Fuel & Tire Rates

Caterpillar Performance Handbook, Ed. 52 (2022)

Industry-standard reference for haul truck specifications. Source of all TRUCK_SPECS defaults (payload, fuel burn L/hr, tire cost/hr, machine rates). Per-class maintenance benchmarks cross-referenced with MINING3 fleet surveys and Cummins service data.

Source for: all TRUCK_SPECS, fuel burn, tire cost defaults

Speed · Tire Life · Water Fleet

GRT (Global Road Technology) Haul Road Management (2024)

Comprehensive haul road management case study including iron ore ramp (Australia): +10 km/h speed gain from road condition improvement, 52% tire life improvement on treated ramp, 51% water truck fleet reduction, full fleet ROI methodology. Conservative industry default for tire life gain per RR point: 12%/pt (GRT upper bound: 52%/pt).

Source for: speed gain default, tire life gain default (12%/pt), water fleet reduction

Grading Reduction · Water Reduction

Envirofluid 56 km Haul Road Case Study

Documented 56 km unsealed haul road treatment project. Outcomes: 87.5% reduction in grader hours, 51% reduction in water truck operations, 6% fuel improvement fleet-wide. Basis for the 85% grading reduction coefficient used in the municipal calculator.

Source for: grading savings formula (85% reduction default)

Fuel Savings · Grade-Adjusted

Cypher Environmental / Shenhua Coal Mine (2015)

Published case study: 17.4% fleet fuel reduction achieved on a 19.8 km haul road after dust suppression and road stabilization treatment. Annual savings: $3.6M USD. Represents the upper bound reference for fuel savings. Conservative default: 3%/pt RR reduction (real-world range: 3–17.4% depending on grade and conditions).

Source for: fuel saving coefficient upper bound

Fuel Savings · Real-World Average

Dust-A-Side & Michelin Controlled Test

Dust-A-Side studies (Brazil and South Africa) document 3–5% average real-world fuel reduction from haul road dust suppression. Michelin controlled test on paved vs. unpaved surface: 7.5% fuel reduction. Combined with Shenhua data, these establish the 3%/pt default for the flat/unknown grade fuel saving coefficient.

Source for: fuel saving default (3%/pt RR for flat roads)

Filter Costs · Per-Class Scaling

Propulsa / Rio Tinto Saguenay (Case Study)

Documented filter and maintenance savings from dust suppression at a Rio Tinto operation. Air filter savings: >$125,000/vehicle/year. Provides the empirical basis for per-class engine air filter cost scaling in the Dust OpEx calculator. Filter costs scale approximately with truck engine displacement and payload class.

Source for: engine air filter cost benchmarks by truck class

Gravel Loss · Municipal Formula

Frontiers in Built Environment (2020)

"Wearing Course Aggregate Loss Models for Unsealed Roads in Australia." Frontiers in Built Environment. Establishes traffic-dependent gravel loss rates: approximately 25 t/km/year at ADT=100, scaling with traffic volume. Replaces the former hardcoded 300 t/km/yr constant that did not account for traffic volume.

Source for: municipal gravel loss formula (25 t/km/yr × traffic factor)

Unsealed Roads · Best Practice

ARRB Unsealed Roads Best Practice Guide (Ed. 2)

Australian Road Research Board guide covering wearing course life, optimal grading frequency, and condition-based maintenance triggers for unsealed roads. Referenced for municipal default grading frequency and road maintenance cost benchmarks.

Source for: municipal grading frequency defaults, road condition parameters

Speed Improvement · Production Data

MaxMine Fleet Analytics Case Study

MaxMine fleet analytics platform case study documenting +10 km/h average speed improvement after haul road condition improvement at an Australian open-cut mine. Corroborates the GRT speed improvement data and validates the 8% speed gain default.

Source for: speed improvement default (8% / +10 km/h case study)

Haul Road Physics · Fuel Model

SAE Paper 2015-01-0050

Technical paper establishing methodology for calculating haul road roughness effects on vehicle fuel consumption. Validates the physics-based grade-adjusted fuel savings coefficient used when haul grade > 0: approximately 65% of total fuel consumed on the loaded uphill leg.

Source for: grade-adjusted fuel savings formula (65% loaded uphill)

Engine Filters · Dust Ingestion

Machinery Lubrication & One Eye Industries

Industry references documenting hydraulic filter bypass failures in dusty conditions (Machinery Lubrication) and engine filter failure modes from dust ingestion leading to accelerated engine wear (One Eye Industries). Support the maintenance reduction estimates for dust OpEx calculations.

Source for: filter change frequency defaults, dust OpEx assumptions

Methodology notes: The calculator uses conservative defaults throughout. Key corrections applied in the current version: (1) fuel and tire savings now apply only to travel hours, not loading/dumping time (travelFraction correction); (2) a grade-adjusted fuel savings coefficient is used when haul grade > 0, based on the physics of loaded vs. empty truck power demand; (3) strip ratio is correctly applied only to mixed ore/waste roads; (4) per-class maintenance and filter cost defaults scale with truck payload class. All defaults can be overridden with site-specific data.