Modeling the Impact of Haul Road Conditions on Mining Vehicle Reliability: A Methodological Framework

Haul road condition is recognised as a driver of tyre wear, fuel consumption and maintenance cost of mining haulage vehicles, but its link to reliability is usually stated qualitatively. Road surface irregularities are classified by wavelength and amplitude, and a dimensionless roughness index Q is defined from defect density and amplitude; for the categories of a published microprofile classification it gives Q ≈ 0.15, 0.50 and 1.00 for good, moderate and poor roads. Reliability is modelled by an additive hazard, which yields R(t|Q) = R0(t)exp(-γQt): the relative reliability decays exponentially in Q at a rate proportional to mission time and remains a probability for all Q ≥ 0. Parameters are estimated by Poisson regression of failure counts on exposure by road category, and a closed-form rule gives the number of failures needed to detect a given rate ratio (about 200 for a 1.5-fold difference at 80 % power). The case data comprise 2737 daily downtime records for four CAT 773E haul trucks at the Sayak mine from 1 January 2023 to 30 September 2026 and 251 component-monitoring records as of 1 September 2026. Consolidation of consecutive records with the same truck and classifier gives 295 technical episodes; tyre-related events are the largest group (30.2 %), while transmission and final-drive events account for 72.5 % of technical downtime. Because the records contain no exposure by road category, they characterise downtime structure and replacement priorities but do not quantify the road effect; a data collection protocol for doing so is proposed. Mid-range speeds of the road categories imply 33 % and 129 % longer travel times on moderate and poor roads.