Developing a robust SHM method for offshore jacket platform using model updating and fuzzy logic system

Structural monitoring is essential for ensuring the structural safety performance during the service life. The process is of paramount importance in case of the offshore jacket-type platforms due to the underwater structural parts subjected to the marine environmental conditions. This work is an experimental investigation on a laboratory model of a jacket platform with the objective of establishing a baseline finite element (FE) model for long-term structural health monitoring for this type of structures. A robust damage diagnosis system is also developed which is less sensitive to both the measurements and the modeling uncertainties. Experimental vibration tests are conducted on a physical platform model to obtain dynamic characteristics and then, the initial FE-model of the intact structure is developed to determine them numerically. Some differences between numerically and experimentally identified characteristics emerge due to various uncertainties in the FE-model and measured vibration data. To minimize these differences, initial FE-model is updated according to the experimental results. The updated FE-model is employed to predict the changes in the dynamic characteristics under variety of damage scenarios which are imposed by reducing the stiffness at the components of the model. Fuzzy logic system (FLS) and probabilistic analysis is developed for linguistic classification of damage and global damage diagnosis. Incorporation of the FLS fault isolation technique into FE-model updating method are proposed and evaluated for two different FLS methods to develop a vigorous damage diagnosis method. The efficiency of the technique is validated by different damage scenarios foreseen on the physical model. This technique is shown to be effective for diagnosing the presence of degradation and quantify it.