Abstract

The performance of haul trucks in open-pit mining is strongly affected by haul-road geometry, surface condition, rolling resistance, and operational traffic regimes. However, existing studies often consider road-surface mapping, vehicle dynamic response, and onboard telemetry as separate information streams, which limits the reproducible assessment of how road-related factors are associated with VIMS-derived fuel-use proxy burden, mechanical dynamic response, and transport-cycle instability. This study proposes a field-based, segment-level multisensor framework that integrates unmanned aerial vehicle/light detection and ranging (UAV/LiDAR) road-surface reconstruction, global positioning system/inertial measurement unit (GPS/IMU) trajectory and vibration data, and Caterpillar Vial Information Management System (VIMS) telemetry into a unified spatiotemporal analytical dataset. The methodological contribution consists in the synchronization of heterogeneous data sources at the road-segment level, the calculation of interpretable road-condition and vehicle-response indicators, and the statistical assessment of road-related effects while explicitly accounting for confounding factors such as longitudinal grade, payload state, speed regime, truck class, and operational variability. Unlike studies that use LiDAR mapping, vibration monitoring, or onboard telemetry as separate diagnostic channels, the proposed approach introduces a segment-level analytical framework in which road morphology, truck response, and operational penalties are aligned within the same spatial unit, interpreted under confounder-aware conditions, and verified through repeat-pass reproducibility and robustness checks. The framework was tested on haul roads around the Ekibastuz open-pit coal mine. The field analysis identifies road segments where degraded surface morphology, increased waviness, unfavorable longitudinal profile, and higher rolling resistance coincide with increased mechanical dynamic response, VIMS-derived fuel-use proxy burden, braking instability, and travel-time variability. The results are interpreted as controlled field-supported associations rather than as isolated causal effects. The proposed maintenance ranking should therefore be regarded as a decision-support output, while the operational effectiveness of specific repair interventions requires future before–after validation.

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