STRUCTURAL ONTOLOGY AND AI-DRIVEN PREDICTIVE ENGINEERING: A FRAMEWORK FOR FUTURE SMART INFRASTRUCTURE

Modern civil infrastructure is increasingly required to operate under conditions of uncertainty driven by climate change, urbanization, and complex socio-technical interactions. Traditional engineering approaches, largely based on static design assumptions and periodic assessment, struggle to address dynamically evolving environments. Recent advances in artificial intelligence, digital twins, and sensor networks have enabled predictive monitoring; however, a unifying theoretical framework explaining how infrastructure systems can continuously adapt and maintain stability remains underdeveloped. This paper proposes a conceptual framework for intelligent infrastructure grounded in a structural ontology perspective, where infrastructure systems are interpreted as dynamic informational structures rather than purely physical entities. Building on principles of recursive self-reference and feedback-driven adaptation, the study introduces a model in which infrastructure maintains an evolving internal representation—or self-model—derived from real-time data streams. This recursive interaction between system state, predictive modeling, and environmental inputs enables continuous learning and anticipatory response, forming the basis of AI-driven predictive engineering. The framework integrates concepts relevant to structural health monitoring, smart cities, and digital twin technologies, demonstrating how adaptive feedback mechanisms can enhance resilience, optimize maintenance strategies, and support multi-hazard risk management. Conceptual applications are discussed for earthquake resilience, urban mobility systems, and climate-responsive infrastructure networks. By connecting philosophical systems theory with contemporary machine learning approaches, the paper provides an interdisciplinary foundation for next-generation civil engineering design, emphasizing infrastructure capable of self-monitoring, prediction, and adaptive stabilization. The proposed perspective contributes toward the development of autonomous and resilient infrastructure systems aligned with emerging smart city paradigms and future engineering practice.