88 2. STATE OF THE ART Conventional inspection practices in mining rely heavily on the inspector's subjective experience and physical methods that involve high logistical costs (scaffolding, aerial work platforms). These practices suffer from human biases and consistency errors that can lead to an underestimation or overestimation of structural risk (Rachman & Ratnayake, 2019). The use of Machine Learning (ML) and Computer Vision in Risk-Based Inspection eliminates this variability. According to Rachman (2019), ML allows for the simultaneous processing of complex variables and degradation patterns, prioritizing assets based on the actual probability of failure. Integrating drones with high-resolution sensors enables data capture in hard-to-reach areas (transfer chutes, milling structures, port terminals), drastically reducing workplace safety risks and allowing for informed decision-making based on a data density impossible to obtain manually. 3. FOUNDATIONS OF THE AIM MODEL: THE THREE DIMENSIONS OF INTEGRITY To build a management model that guarantees operational sustainability, it is necessary to delve deeper into the technical hierarchy of integrity. Following the principles of El-Reedy (2022) and the CCPS (2016), the AIM model is based on three interconnected dimensions: 3.1. Design Integrity This dimension constitutes the asset's baseline. It focuses on ensuring that the infrastructure possesses the necessary mechanical and structural capacity to fulfill its purpose under the anticipated conditions throughout its useful life. 3.1.1. Standards and Regulations: It involves verifying that the design complies with international codes (AISC for steel, ACI for concrete) and local regulations. 3.1.2. Selection of Materials Evaluate whether the choice of materials and protection systems (galvanizing, epoxy coatings) is compatible with the aggressiveness of the mining environment (presence of chlorides, extreme pH in leaching processes or critical humidity). 3.1.3. Documentation Management Design integrity depends on the availability and accuracy of as-built drawings. The proactive model uses these documents to validate whether operational modifications made over the years (e.g., increased conveyor belt capacity) have eroded the original safety factor. 3.2. Technical Integrity It represents the physical health of structures at a given moment. It is the dimension where AI and drone technology generates its greatest value. 3.2.1. Deterioration Mechanisms The technical analysis focuses on identifying and quantifying degradation mechanisms such as corrosion (uniform or pitting), structural fatigue due to mechanical vibration, and abrasive wear.
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