Zafarullah Nizamani

Tubular Strength Comparison of Offshore Jacket Structures Under API RP 2A and ISO 19902

Offshore platforms are only 67 years old and are fairly new compared to other types of civil engineering structures. Offshore Jacket platforms in Malaysia are designed using API RP2A Working Stress Design (WSD) code. API WSD code has proved its effectiveness and has been in use for long time, but it needs to be changed into load and resistance factor design (LRFD)-based code which is being followed by all building code agencies. In place of WSD, limit state design or LRFD has proved to be more rational as it considers probabilistic models. The reliability of Jacket platforms is maintained in API RP 2A-LRFD by setting target safety factor the same as that provided in WSD, which means structures designed as per LRFD code will have the same reliability as API RP 2A-WSD (which has already provided safe structures and the best available practice for design). When adopting LRFD methodology, the appropriate load and resistance factors can be optimised through the process of calibration. Knowledge of the strength equations in the different codes and the similarities and differences between them is useful for the calibration. The first step in the calibration process is the determination of reliability of structural tubular members of the Jacket designed as per existing practice of WSD and LRFD code. In this text, API RP 2A-WSD code and International Standard Organization (ISO 19902) (LRFD-based code) are taken into consideration for the reliability analysis. The relevant strength equations of three codes are identified and compared, and the similarities and differences are determined for tubular members which are the main part of Jacket structures.

Determination of joint distribution for operating metocean variables for offshore structures

Determination of statistical properties of operating and extreme metocean environmental variables and their interaction with offshore and coastal structures is critical for their design. The mean values of wave height and wave periods increase with increasing intensity of the storm, however, other variables such as wind and current may increase or decrease at the same time. Furthermore, these variables may change their directions during that period and may act in the same or in opposite directions. This paper considers operating conditions of the design of offshore structures. At present design codes recommend three different methods for determination of statistical properties of metocean variables. The most common is based on a combination of metocean parameters estimated from distributions independently without considering any joint probability another one is considering the joint probability of variables and the last one is considering load and resistance both simultaneously. Here in this study, a seco...

Uncertainty Modelling of Load

This chapter deals with statistical data analysis for load variables. The random variables are analysed with statistical distributions evaluated. The parameters of load distributions like mean and standard deviations are determined. An extrapolation up to 1,000 years of return period is made using Weibull and Gumbel distributions along with their parameters.

Uncertainty Modelling of Resistance

Resistance or strength parameters in LRFD are taken as random variables. Uncertainty modelling is the most important step for reliability analysis. The random variables are analysed, and range of type of distributions, mean values and standard deviations are discussed.

Extension of Life of Jacket Platforms

Jacket platforms are frequently checked when loading and resistance parameters are changed or at the end of design life and if hydrocarbon reserves are still there to be extracted, it must be checked for extension of life. Therefore, the probability of failure is used to check its strength at all the stages. Bayesian updating is a technique to be used for updating probability of failure taking into consideration probability of failure.

Joint Reliability Analysis and Environmental Load Factor

Due to critical nature of joints, API and ISO code recommend them to be stronger than components. The joint types are K-, T/Y- or X-joint, and they are classified as based on the geometry and loads acting on the member. The joints are analysed for four types of stresses. Their respective environmental load factors have been determined and reported.

System Reliability-Based Environmental Loading

Codes applicable to Jacket platforms, such as API WSD, API LRFD and ISO 19902, are based on component and joint design. If Design codes are followed properly, the strength of member will always exceed the load effect as utility ratio is always maintained less than one while designing the Jacket. Codes consider overall structural integrity, redundancy and multiple failure paths only indirectly by using structural integrity assessment methods. Before going for reassessment in this chapter environmental load factor has been evaluated using system reliability.

Component Reliability and Environmental Load Factor

Behaviour of structure can be measured by probability of failure or reliability index. Target reliability of components is found as per API RP2A WSD. Load factors are developed in such a way that the reliability index of Jacket is at predefined target level. The reliability indices of ISO LRFD design for a range of load factors are determined. When ISO load factors are plotted against the corresponding API target reliabilities, the intersection point gave the proposed load factor. Not only the Jacket reliability designed as per new load factors will be higher than the target, but also it will ensure safer Jacket.

Reassessment of Remaining Strength of Subsea Corroded Pipeline using Bayesian Updating

System strength evaluation of subsea pipeline, which has already completed its design life, is an important issue to deal with especially when hydrocarbon is the material to be transported. The remaining strength of pipeline in terms of probability of failure can be determined using assessment of maximum operating pressure and its capacity by using burst test results. Monte Carlo simulation is used to find probability of failure and then with burst test results the existing probability of failure can be updated using Bayesian updating technique.