Intelligent Conceptual Architecture as a Proposal for Integration of Artificial Intelligence for Accident Prevention with Heavy Equipment

Resumo (EN)

Abstract. Operations with heavy load-handling machinery such as reach stacker forklifts, mobile port cranes (MHCs), and automated industrial vehicles are among the most critical topics in ports and industrial environments, being responsible for high rates of operational accidents. This article aims to present an intelligent and integrated functional architecture, based on Artificial Intelligence (AI), aimed at proactive accident prevention through the combination of four modules: real-time hazard detection, predictive maintenance, operator behavioral analysis, and intelligent decision support. The methodology used is based on a conceptual modeling validated by high-impact scientific literature, complemented by the application of the Analytic Hierarchy Process (AHP) method, which allowed the hierarchiza
tion of critical decision criteria such as risk reduction (26.32%), adaptive learning (26.32%) and adherence to regulatory standards (23.64%). The results indicated computer vision (43.39%) and predictive maintenance (26.70%) as the technologies with the highest strategic priority. The proposal is in line with the milestones of Industry 4.0, ISO 12100, and ISO 13849–1 standards and guidelines, consolidating itself as a replicable, scalable, and high-impact solution for environments that demand high standards of safety, traceability, and operational efficiency.