RELIABILITY METHODS FOR SHIP STRUCTURES

Abstract

ABSTRACT The ever increasing use of high‐strength materials and advanced technologies in surface ship structural design requires a very careful and systematic analysis to insure that levels of safety are maintained. This is much more easily said than done. The application of new technologies will not allow extrapolation of existing design criteria. Due to the uncertainties involved with future loading conditions, material properties, quality of workmanship in construction, and the limitations in numerical methods of analysis, the absolute safety of a structure cannot be established. It would therefore seem appropriate to use methods of analysis which would attempt to account for the various uncertainties and which allow the designer to limit the risk of unacceptable consequences. Estimation of this risk, even if used only to compare design alternatives, can be a useful and economical tool. Structural reliability has its roots in the fields of civil and aerospace engineering and has made great strides forward in the last decade. Many methods have been proposed to evaluate the risk of structural failure. These methods include: first order second moment (FOSM), advanced second moment (ASM), and Monte Carlo simulation using both conditional expectation and antithetic variates techniques for variance reduction. All of these methods consider the type of the problem, the various parameters involved, and the uncertainties associated with these parameters. In estimating the risk, the uncertainties are modeled as random variables with mean values, variance, and probability density and distribution functions. Each method uses this information in a different manner, involving some assumptions and limitations. In this paper we evaluate the available methods as to their suitability for estimating the risk of structural failure in ships. The merits and shortcomings of each method are discussed and each is then used to solve a simple example problem. The most effective method is chosen for more advanced work in this field.

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