Conceptual Framework for Smart Infrastructure Systems Using AI-Driven Predictive Maintenance Models

作者：Adepeju Nafisat Sanusi, Olamide Folahanmi Bayeroju, Zamathula Queen Sikhakhane Nwokediegwu · 发表于：International Journal of Advanced Multidisciplinary Research and Studies · 年份：2023 · DOI：10.62225/2583049x.2023.3.1.4911 · 被引用次数：6

The increasing complexity and scale of modern infrastructure systems present significant challenges for ensuring efficiency, resilience, and longevity. Traditional maintenance approaches, often reactive or preventive, are resource-intensive and limited in their ability to anticipate failures in dynamic environments. Recent advances in artificial intelligence (AI) offer transformative opportunities for predictive maintenance, enabling infrastructure systems to transition from static operations to adaptive, data-driven management. This paper proposes a conceptual framework for smart infrastructure systems that integrates AI-driven predictive maintenance models to optimize performance, reduce costs, and enhance sustainability. The framework emphasizes four interrelated dimensions. First, data acquisition and integration harness sensor networks, Internet of Things (IoT) devices, and historical records to capture real-time operational parameters. Second, AI-driven analytics employ machine learning, deep learning, and anomaly detection to forecast component degradation, predict failure probabilities, and prioritize interventions. Third, decision-support mechanisms link predictive insights with governance and operational structures, guiding resource allocation, scheduling, and risk management across infrastructure assets. Finally, feedback and continuous learning loops enable adaptive improvement by incorporating new data into evolving models, ensuring resilience against environment...