Quantum Potential Index – Qpi: Measuring What Cpuld Have Happened Applied to Fatigue and Drowsiness in 24/7 Mining Haulage Systems WA Lino, Proposed by the author Department of Occupational Health and Safety, Las Bambas Mining, Peru (Presenting author: walter.lino@mmg.com) Abstract Fatigue and drowsiness remain critical factors in high-potential events within continuously operating, heavy-duty mining haulage systems. Although in-cab monitoring systems allow for real-time detection of fatigue states, their application is predominantly reactive and focused on the individual. This study presents a conceptual application of the Quantum Potential Index Applied to Fatigue (QPIFAT) aimed at anticipating high-potential systemic states associated with fatigue and drowsiness before critical alarms occur. Conceptually inspired by state superposition principles, QPIFAT integrates observable, latent, human, and environmental variables to represent preventative configurations prior to operational collapse. The approach is currently in the design and pilot planning phase, with trials scheduled to begin in 2026. This version describes the theoretical framework, the proposed methodology, and a conceptual simulation based on representative patterns of the OAS (Operator Alert System) currently in use at Minera Las Bambas, maintaining technical neutrality and without premature empirical conclusions. Keywords Fatigue, drowsiness, mining haulage, potential states, predictive risk, SIF-P. 1. INTRODUCTION Fatigue management in the mining industry has evolved significantly over the last decade through the incorporation of in-cab detection systems and continuously operating (24/7) control centers. These systems have enabled the real-time identification of drowsiness and the activation of intervention protocols at the individual level. However, despite these advances, high-potential fatigue-related events continue to occur, primarily as a result of the systemic accumulation of operational stresses that are not always immediately or readily apparent. In this context, the need arises to complement predominantly reactive approaches with models capable of anticipating dangerous system states before they collapse into critical events. Fatigue thus ceases to be understood exclusively as an individual condition and is addressed as an emergent phenomenon of the work system, influenced by multiple variables that interact simultaneously. The operational context analyzed corresponds to a large-scale mining operation characterized by: 3
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