Evaluation of the Performance of the Inverse Vibration Algorithm in Damage Detection of a 2D Shear Model in a Damped Offshore Platform

Fixed-jacket offshore platforms are among the most complex civil engineering structures, subjected to various damaging loads throughout their operational lifespan. The failure of such structures can lead to catastrophic consequences, including significant loss of human life and severe environmental and economic damages. Given the critical importance of uninterrupted oil extraction on these platforms, continuous structural health monitoring (SHM) has become a fundamental necessity. Effective SHM requires a thorough understanding of the platform's vibrational response, which is inherently dependent on its intrinsic and dynamic properties, including stiffness, mass, damping matrices, and natural frequencies. Without access to these fundamental parameters, an accurate assessment of the dynamic behavior of the platform remains unattainable. One of the key challenges in offshore structural health monitoring is the precise identification and estimation of these dynamic parameters under real-world environmental and operational loads. Damage detection techniques rely heavily on changes in these parameters to diagnose structural deficiencies at an early stage. The inverse vibration method has emerged as a promising approach for damage detection by evaluating variations in dynamic properties and structural matrices. This method leverages modal data obtained from structural responses to irregular and environmental wave loads to identify and localize potential damage. Given that offshore platforms are subjected to complex and highly variable loading conditions, robust numerical and computational approaches are required to extract meaningful insights from vibrational data. In this study, the proportional Rayleigh damping model was employed as a method for estimating the stiffness matrices before and after structural damage. Rayleigh damping is a widely used technique in structural dynamics that considers mass and stiffness proportional damping. By implementing inverse vibration methods, this study systematically analyzed structural behavior under dynamic loading conditions. The effectiveness of this approach was evaluated through numerical simulations on simplified and constrained 2D shear models. These models served as an idealized representation of offshore platforms, enabling controlled assessment of the proposed methodology. The results of this study revealed a significant correlation between the damping ratio and the accuracy of damage detection calculations. Specifically, increasing the damping ratio within the software model led to a noticeable reduction in the precision of the damage identification process. This finding underscores the importance of carefully selecting damping parameters when implementing inverse vibration methods for real-world applications. Furthermore, the study highlights the need for refined computational techniques that can mitigate the adverse effects of high damping on damage detection accuracy. Overall, this research contributes to the advancement of offshore structural health monitoring by demonstrating the feasibility of inverse vibration methods in detecting damage through changes in dynamic properties. The study also provides valuable insights into the limitations posed by high damping ratios in numerical models and underscores the importance of refining SHM techniques for enhanced reliability. The findings presented in this work can serve as a foundation for future investigations into advanced numerical modeling approaches and experimental validations aimed at improving damage detection capabilities in offshore structures. By integrating high-fidelity simulations with field data, future research can further enhance the applicability of these methodologies in real-world offshore engineering scenarios, ultimately improving the safety and resilience of offshore platforms.