Role of Reliability Analysis in Structural Design

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Modern structures require more critical and complex designs; the need for accurate and efficient approaches to assess uncertainties in loads, geometry, material properties, manufacturing processes involved and also the operational environment, has increased significantly. Reliability assessment techniques help to develop the initial guidance for robust designs. In this context, the classical methods such as theory of probability, statistical methods and reliability analysis methods are often used by structural engineers. Some of the methods which have been developed in the later stages include Monte Carlo Sampling, Latin Hyper Cube Sampling, First and Second Order Reliability Methods, Stochastic Finite Element Method and Stochastic Optimization. In addition, in those structural problems where randomness is relatively small, a deterministic model is usually used rather than a Stochastic Model. However, when the level of uncertainty is high, Stochastic approaches are necessary for system analysis and design. Number of probabilistic analysis tools have been developed to qualify uncertainties, but the most complex systems are still designed with simplified rules and schemes, such as factor of safety based designs. However, these traditional design processes do not directly account for the random nature of the most input parameters. Factor of safety is used to maintain some degree of safety in the structural design. Generally, the factor of safety is understood to be the ratio of the expected strength of response to the expected load. In practice, both the strength and load are variables, the values of which are scattered about their respective mean values. When the scatter of the variables is considered, the factor of safety could potentially be less than unity and the traditional factor of safety based design would fail. More likely is that the factor of safety is too conservative, which leads to an over expensive design.