Vibration-based damage identification technique, categorized as parametric and nonparametric methods, plays an important role in guaranteeing the success of offshore operations and the integrity management of marine structures. In the parametric approaches, modal parameters and their derivative are usually applied to obtain the damage-sensitive features. Nevertheless, there are still some challenging problems for the parametric method in application. Nonparametric (data-driven) approaches are aim to extract damage sensitive features directly from the measured data. For the data-driven approaches, it is vital to find which indicator derived from the response signal is sensitive to structural damage. Considering the derivatives of strain are more sensitive to small structural damage than that of displacement, the strain-related damage detection methods have currently gotten more attention. In this study, a principal strain data-driven method is proposed, which is derived from strain energy dispersion theory and directly employs the time history data of three principal strains of an intact and damaged structure to construct the damage identification indicator. The efficiency of the method is validated by a numerical offshore pile structure with several damage cases considering noise scenarios. Due to the difficulty of determine the correct three principal strains in the real experimental measurements, the approximation damage indicators are constructed based on one, two or three kinds of strain response among the axial, radial and hoop strains response, instead of the three principal strains response. The results show that satisfying damage identification results can still be gotten for both the single- and multiple-damage scenarios using only axial or hoop strain response even under noise conditions. The proposed principal strain data-driven damage identification method can be used as a viable and effective technique for damage localization and severity estimation of marine structures.

Risk-informed design approaches are comprehensively implemented in the design and verification process of HPR1000 nuclear power plant. Particularly, Level 2 PSA is applied in the optimization of severe accident prevention and mitigation measures to avoid the extravagant redundancy of system configurations. HPR1000 preliminary level 2 PSA practices consider internal events of the reactor in the context of at-power condition. Severe accidents mitigation and prevention system and its impact on the overall large release frequency (LRF) level are evaluated. The results showed that severe accident prevention and mitigation systems, such as fast depressurization system, the cavity injection system and the passive containment heat removal system perform well in reducing LRF and overall risk level of HPR1000 NPP. Bypass events, reactor rapture events, and the containment bottom melt-through induced by MCCI are among the dominant factors of the LRF. The level 2 PSA analysis results indicate that HPR1000 design is reliable with no major weaknesses.

Modal parameters identification of offshore structures is important for many engineering applications, such as damage detection, structural health monitoring, etc. Operational modal analysis has been widely used for large structures. However, measured signals are inevitably contaminated with noise and may not be clean enough for identifying the modal parameters with proper accuracy. The traditional methods to estimate modal parameters in noisy situation are based on over-determined system to absorb the “noise modes” firstly, and then using the stability diagrams to distinguish the true modes from the “noise modes”. However, it is difficult to sort out true modes when the signal noise ratio is low, especially, the “noise modes” will also tend to be stable as the model order increases. This study develops a noise reduction procedure for polyreference complex exponential (PRCE) modal analysis based on ambient vibration responses. In the procedure, natural excitation technique (NExT) is firstly applied to get free decay responses (auto- and cross-correlation functions) from measured (noisy) ambient vibration data, and then the noise reduction method based on solving the partially described inverse singular value problem (PDISVP) is implemented to reconstruct a filtered data matrix from the measured data matrix. In our case, the measured data matrix is block Hankel structured, which is constructed based on the free decay responses. The filtered data matrix should maintain the block Hankel structure and be lowered in rank. When the filtered data matrix is obtained, the PRCE method is applied to estimate the modal parameters. The proposed NExT-PDISVP-PRCE scheme is applied to field test of a jacket type offshore platform. Results indicate that the proposed method can improve the accuracy of operational modal analysis.