| Abstract: | Monitoring offshore platforms, long span bridges, high rise buildings, TV towers and other similar structures is essential for ensuring their safety in service. Continuous monitoring assumes even greater significance in the case of offshore platforms, which are highly susceptible to damage due to the corrosive environment and the continuous action of waves. Also, since a major part of the structure is under water and covered by marine growth, even a trained diver cannot easily detect damage in the structure. In the present work, vibration criterion is adopted for structural monitoring of jacket platforms. Artificial excitation of these structures is not always practicable and ambient excitation due to wind and waves may not be sufficient for collecting the required vibration data. Alternate methods can be adopted for the same purpose, for example, the application of an impact or a sudden relaxation of an applied force for exciting the structure. For jacket platforms, impact can be applied by gently pushing the structure at the fender while relaxation can be accomplished by pulling the structure and then suddenly releasing it using a tug or a supply vessel in both cases. The present study is an experimental investigation on a laboratory model of a jacket platform, for exploring the feasibility of adapting vibration responses due to impulse and relaxation, for structural monitoring. Effects of damage in six members of the platform as well as changes in deck masses were studied. A finite element model of the structure was used to analyze all the cases for comparison of the results as well as system identification. A data acquisition and analysis procedure for obtaining the response signatures of the platform due to the impulse and relaxation procedure was also developed for possible adoption in on-line monitoring of offshore platforms. From the study, it has been concluded that both impulse and relaxation responses are useful tools for monitoring offshore jacket platforms. The present work forms the basis for the development of an automated, on-line monitoring system for offshore platforms, using neural networks. |
