Offshore structures are exposed to random wave loading in the ocean environment and hence the probability distribution of the extreme values of their response to wave loading is required for their safe and economical design. This paper investigates the suitability of the Gumbel, the Generalized Extreme Value (GEV), and the Generalized Pareto (GP) distributions for modelling of extreme responses by comparing them with empirical distributions derived from extensive Monte Carlo time simulations. It will be shown that none of these distributions can model the extreme values adequately but that a mixed distribution consisting of both GEV and GP distributions seems to be capable of modelling the extreme responses with very good accuracy.
The methodology for Reliability-Based Design and Assessment (RBDA) of an ageing fixed steel offshore structure was established to support detailed re-assessment applied to the management of the structure’s safety, integrity analysis and reliability by evaluating the loading acting on the structure. It is a tool for the high-end analysis of the structure for risk-based design assessment and has been succesfully implemented in the North Sea under Shell operating company. The main purposes of RBDA are to manage a structure’s risk level over its remaining service life and to initiate the cost-efficient inspection or mitigation actions (if required). This method consists of Type I and II uncertainties used to determine the probability of failure for the structure over its remaining service life. However, limited work has been done so far on its application at many different regions, particularly in the South East Asia. Therefore, this paper investigates the robustness of the RBDA methodology applied to fixed offshore structures at shallow waters of Malaysia by considering the native environmental criteria, local authorities’ obligation and company requirements. It is shown that this procedure can efficiently assist in understanding the structure’s failure mechanism and correctly define the relevant type of mitigations required.
Offshore structures are exposed to random wave loading in the ocean environment and hence the probability distribution of the extreme values of their response to wave loading is required for their safe and economical design. Due to nonlinearity of the drag component of Morison’s wave loading and also due to intermittency of wave loading on members in the splash zone, the response is often non-Gaussian; therefore, simple techniques for derivation of the probability distribution of extreme responses are not available. To this end, the conventional Monte Carlo time simulation technique is frequently used for predicting the probability distribution of the extreme responses. However, this technique suffers from excessive sampling variability and hence a large number of simulated response records are required to reduce the sampling variability to acceptable levels. This paper takes advantage of the correlation between extreme responses and their corresponding extreme surface elevations to derive the probability distribution of the extreme responses accurately and efficiently, i.e. without the need for extensive simulations.
Linear Random Wave Theory (LRWT) is frequently used to simulate water particle kinematics at different nodes of an offshore structure from a reference surface elevation record. It is, however, well known that wave kinematics calculated from LRWT suffer from unrealistically large high-frequency components in the vicinity of mean water level. To overcome this deficiency, a common industry practice consists of using linear wave theory in conjunction with empirical techniques, such as the Wheeler or the vertical stretching methods, to provide a more realistic representation of the near-surface water particle kinematics. In this paper, a modified version of LRWT is introduced, which, unlike the standard LRWT, does not lead to unrealistically large high-frequency components in the vicinity of mean water level. The proposed method leads to predicted kinematics in the near surface zone which lie between corresponding values from the Wheeler and the vertical stretching methods, respectively.
Offshore structures are exposed to random wave loading in the ocean environment, and hence the probability distribution of the extreme values of their response to wave loading is of great value in the design of these structures. Due to nonlinearity of the drag component of Morison wave loading and also due to intermittency of wave loading on members in the splash zone, the response is often non-Gaussian; therefore, simple techniques for derivation of their extreme response probability distributions are not available. However, according to a recent paper, in the absence of current, the response of an offshore structure exposed to Morison wave loading, can be approximated by the response of an equivalent finite-memory nonlinear system (FMNS). These models can then be used, with great efficiency, to determine the probability distribution of response extreme values. In this paper, the progress made so far in extending these FMNS models to account for the effect of current on response is discussed.
Offshore structures are exposed to random wave loading in the ocean environment and hence the probability distribution of the extreme values of their response to wave loading is required for their safe and economical design. To this end, the conventional simulation technique (CTS) is frequently used for predicting the probability distribution of the extreme values of response. However, this technique suffers from excessive sampling variability and hence a large number of simulated response extreme values (hundreds of simulated response records) are required to reduce the sampling variability to acceptable levels. A more efficient method (ETS) was recently introduced which takes advantage of the correlation between the extreme values of surface elevation and their corresponding response extreme values. The method has proved to be very efficient for high-intensity sea states; however, the correlation and hence the efficiency and accuracy of the technique reduces for sea states of lower intensity. In this paper, a more efficient version of the ETS technique is introduced which takes advantage of the correlation between the extreme values of the nonlinear response and their corresponding linear response values.
For an offshore structure, wind, wave, current, tide, ice and gravitational forces are all important sources of loading which exhibit a high degree of statistical uncertainty. The capability to predict the probability distribution of the response extreme values during the service life of the structure is essential for safe and economical design of these structures. Many different techniques have been introduced for evaluation of statistical properties of response. In each case, sea-states are characterised by an appropriate water surface elevation spectrum, covering a wide range of frequencies. In reality, the most versatile and reliable technique for predicting the statistical properties of the response of an offshore structure to random wave loading is the time domain simulation technique. To this end, conventional time simulation (CTS) procedure or commonly called Monte Carlo time simulation method is the best known technique for predicting the short-term and long-term statistical properties of the response of an offshore structure to random wave loading due to its capability of accounting for various nonlinearities. However, this technique requires very long simulations in order to reduce the sampling variability to acceptable levels. In this paper, the effect of sampling variability of a Monte Carlo technique is investigated.
Child car occupants must be secured using appropriate Child Restraint Systems (CRS) in order to reduce the risk of severe injuries in the case of crashes or emergency braking. As part of ASEAN NCAP protocol, the so-called "CRS Reference List" of widely available, well performing child seats was established to assess a vehicle’s ability to safely and correctly accommodate child seats. In 2017, ASEAN NCAP introduced a new upper and lower limit crash pulse curve to better represent crashworthiness of cars available in the ASEAN market. With this new curve, the CRS Reference List must be reviewed accordingly; which is the aim of this project. This paper describes the process to update the CRS Reference List, starting from exploring the potential of CRS in three ASEAN markets (i.e., Malaysia, Thailand and Indonesia). The identified CRS were then evaluated based on several selection criteria (i.e., availability, regulation approved, weight group combination, price, size and installation direction). Based on the evaluation, several CRS are shortlisted for further technical assessment following ASEAN NCAP Child Occupant Protection (COP) protocol. The CRS shortlist is presented in this paper. However, the proposed CRS Reference List will not be disclosed in this work.
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