2343 QUANTUM POTENTIAL INDEX – QPI: MEASURING WHAT CPULD HAVE HAPPENEDAPPLIED TO FATIGUE AND DROWSINESS IN 24/7 MINING HAULAGE SYSTEMS WA Lino 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: • Two open pits in simultaneous operation. • A haulage system that operates continuously (24/7), with day and night shifts. • More than 1,100 operators exposed, including 178 mining truck operators. • A dispatch center that monitors the fatigue alert system in real time. • The presence of risk-amplifying factors, such as long ramps, mixed traffic, presence of water on the runway, and variability in the experience and age of the operators. The OAS system classifies fatigue events into different severity levels and activates pause and control protocols from the control center. However, historical analysis of its reports reveals limitations inherent in an event-centric approach, such as: • Repeat offenses by operators with multiple fatigue alerts. • Concentration of alarms at certain times, shifts and months.
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