BEYOND THE INITIAL DESIGN: ASSET INTEGRITY MANAGEMENT FOR MINING INFRASTRUCTURE RESILIENCE *LF Villarreal 1 1 Asset Optimization & Operational Readiness, Ausenco, Peru, (*Presenting author: felipe.villarreal@ausenco.com) ABSTRACT The mining industry faces the global challenge of producing minerals faster, smarter, and more responsibly. In this context, the resilience of static infrastructure is critical. This paper presents an Asset Integrity Management (AIM) model aligned with the pillars of the World Mining Council 2026 (WMC 2026). The methodology breaks the reactive inspection paradigm by adopting a proactive, four-stage approach that integrates smart operations, employing drones and Artificial Intelligence (AI) for data-driven decision-making. The model enables the predictive identification of structural degradation and utilizes computer vision algorithms, integrating these findings into a robust governance framework based on ISO 55001. The paper demonstrates that a proactive AIM model not only optimizes profitability by reducing lost profits but also serves as a pillar of health, safety, and well-being. The case study of a leading polymetallic operation demonstrates that technical proactivity and digitalization are fundamental to the responsible mining of the future. KEYWORDS Asset Integrity, Artificial Intelligence, Sustainable Infrastructure, World Mining Congress. 1. INTRODUCTION AND STRATEGIC CONTEXT The World Mining Congress 2026 underscores the urgent need for smarter and more responsible mining. Traditionally, mining infrastructure has been managed under a "design and forget" approach. However, as the Center for Chemical Process Safety (CCPS, 2016) guidelines point out, asset integrity must be an ongoing effort throughout the entire lifecycle to prevent lowfrequency but high-consequence events. Asset Integrity Management (AIM) is defined as the ability of an asset to perform its intended function effectively and efficiently, while protecting life and the environment (El-Reedy, 2022). This paper proposes a management model that goes beyond human visual inspection, using AI-powered image processing to anticipate failures and implement early mitigation plans. In open-
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