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Offshore structures usually are implemented in harsh environmental conditions which make the structures disposed to different failures. Negligible failures may lead to catastrophic ones. To prevent potential catastrophic failure, development of robust damage detection methods for offshore structures is necessary. Damages change structure's dynamic characteristics. Among different dynamic characteristics, modal frequencies and mode shapes are utilized more often. Due to various problems regarding damage detection methods for offshore structures such as sensor installation and data acquisition in water, damage detection methods using limited sensors seems very vital. In this paper, a novel method called Improved Modal Strain Energy Decomposition Method (IMSEDM) is developed to detect structural damage of jacket-type offshore platforms using a limited number of sensors just above the water. The proposed method has two main contributions. The first is simultaneous use of both modal frequencies and mode shapes which make damage detection more accurate. The second is decomposition of modal strain energy into axial and bending. IMSEDM locates damaged elements by considering the impacts of the damage on axial and bending modal strain energy. The method is evaluated using an experimental jacket type offshore platform. The results confirm that IMSEDM can effectively detect damages with incomplete data. • A new method for damage detection of jacket type offshore platforms. • Limited sensors above the water level to overcome data acquisition difficulties. • 2-directional acceleration sensors signal processing to extract modal data. • Expanding limited experimental modal data using numerical model in ABAQUS. • Demonstrating the method sensitivity to the distance between damages and sensors. [ABSTRACT FROM AUTHOR]

Semi-active control is one of the most effective methods for damage reduction in offshore platforms subjected to intense environmental forces. Despite its advantages, probable device time delays may drastically decrease the real performance of the control algorithm. Thus, the uncertainty could make the control process non-optimal. In this paper, a Kalman Filter is used to ponder previous responses and the history of the measured errors in order to estimate the real state variables of an offshore structure equipped with Magneto-Rheological (MR) dampers. In the current article, the amount of applied voltage to the MR damper is optimized via the Adaptive Particle Swarm Optimization (APSO) method. Furthermore, the structure is controlled by the Linear–Quadratic Regulator (LQR) algorithm. A newly installed offshore platform, located in the Persian Gulf at a depth of 64 m is considered as an example to demonstrate the performance of the controller. The offshore structure is assumed to be excited by near- and far-field earthquakes. The results of the parametric studies indicate that all the earthquake-induced vibrations of the platform can be effectively suppressed by the designed control system. Moreover, the life spans of the dampers may increase with the predicting-optimizing algorithm. A predictor-optimizing semi-active control system is designed to reduce structural responses of a fixed metal offshore platform equipped by horizontal MR dampers under earthquake excitations. The controller estimates the responses of the structure considering probable noises to reduce the time delays and inefficient actuator forces. In addition, the inlet voltages of the dampers are optimized to develop the dampers' duration and reduce the structural responses. Image 1 • Optimizing voltages of MR dampers placed on offshore platforms. • Structural state estimation for improving the efficiency of a control system. • Considering instrument noises and time delays to reach the real-time control. • Optimum-state estimating controller on a structure under far/near-field earthquakes. • Life span increase of semi-active Magneto-Rheological dampers. [ABSTRACT FROM AUTHOR]

With the help of Structural Health Monitoring (SHM) methods, it is possible to identify the occurrence of damage at its early stages and prevent fatality and financial damages. Great advances in signal processing methods in combination with Machine learning tools have led to better achieve this goal. In the present paper, the two major techniques, that is, Empirical Mode Decomposition (EMD) and Discrete Wavelet Transform (DWT) are combined with Artificial Neural Network (ANN) through processing raw acceleration responses measured on a scaled jacket type offshore platform which was constructed and tested as a benchmark structure at K.N. Toosi University of Technology. In this way, ANN was trained by the signals obtained from EMD and DWT for three different conditions of the jacket platform to determine the relative damage severity. The envelope of the obtained signal's energy (ENV) as an appropriate damage index was used to determine the damage location. The results of the application of this procedure on the case study indicated that DWT, compared to EMD, is a more reliable signal processing method in damage detection due to better noise reduction. [ABSTRACT FROM AUTHOR]