PREDICTIVE REANALYSIS IN STRUCTURAL DYNAMICS

Predictive reanalysis has emerged as a vital computational strategy in structural dynamics, enabling efficient updates of structural response predictions following minor modifications in geometry, material properties, or boundary conditions, without resorting to full re-computation. Traditionally rooted in finite element methods, reanalysis techniques have evolved through the integration of Artificial Intelligence (AI) models, offering unprecedented speed and adaptability in dynamic system assessments. This paper provides a comprehensive overview of predictive reanalysis approaches, with an emphasis on recent AI-assisted methodologies. The synergy between data-driven models such as neural networks, decision trees and ensemble learning and physics-based simulations enables more accurate prediction of structural behavior under varying operational scenarios. The application of machine learning has demonstrated significant potential in reducing computational costs, increasing adaptability and enhancing real-time monitoring capabilities in engineering systems. A numerical case study is presented, involving a cantilever beam discretized into five finite elements. The analysis explores how changes in cross-sectional properties at various segments affect the first natural frequency. Predictive AI models are employed to estimate frequency shifts and their performance is compared against classical empirical formulas. The results validate the ability of trained AI models to generalize the influence of structural variations and support decision-making in early design or maintenance phases. The study also highlights current challenges in predictive reanalysis, including data scarcity, model interpretability and integration with real-time monitoring systems. Future directions are outlined, focusing on hybrid modeling techniques, improved data acquisition strategies and the development of standardized benchmarks for AI-assisted structural reanalysis. Ultimately, this work contributes to the growing body of research bridging computational mechanics and machine intelligence, fostering more resilient, adaptive and efficient structural systems.