Reliability analysis of structures by iterative improved response surface method

Offshore and marine renewable energy applications are governed by a number of uncertainties relevant to the design process and operational management of assets. Risk and reliability analysis methods can allow for systematic assessment of these uncertainties, supporting decisions integrating associated consequences in case of unexpected events. This paper focuses on the review and classification of such methods applied specifically within the offshore wind industry. The quite broad differentiation between qualitative and quantitative methods, as well as some which could belong to both groups depending on the way in which they are used, is further differentiated, based on the most commonly applied theories. Besides the traditional qualitative failure mode, tree, diagrammatic, and hazard analyses, more sophisticated and novel techniques, such as correlation failure mode analysis, threat matrix, or dynamic fault tree analysis, are coming to the fore. Similarly, the well-practised quantitative approaches of an analytical nature, such as the concept of limit states and first or second order reliability methods, and of a stochastic nature, such as Monte Carlo simulation, response surface, or importance sampling methods, are still common practice. Further, Bayesian approaches, reliability-based design optimisation tools, multivariate analyses, fuzzy set theory, and data pooling strategies are finding more and more use within the reliability assessment of offshore and marine renewable energy assets.

A review of reliability-based methods for risk analysis and their application in the offshore wind industry

Uncertainty is inevitable at every stage of life cycle development of a product. To make use of probabilistic information and to make reliable decisions by incorporating decision maker's risk attitude, methods for propagating the effect of uncertainty are therefore needed. When designing complex systems, the efficiency of methods for uncertainty propagation becomes critical. In this paper, a most probable point (MPP)-based uncertainty analysis (MPPUA) method is proposed. The concept of the MPP is utilized to generate the cumulative distribution function (CDF) of a system output by evaluating probability estimates at a serial of limit states across a range of output performance. To improve the efficiency of locating the MPP, a novel MPP search algorithm is presented that employs a new search algorithm and a search strategy. A mathematical example and an engine design problem are used to verify the effectiveness of the proposed method. The proposed MPPUA method will serve as a useful tool for propa...

A Most Probable Point-Based Method for Efficient Uncertainty Analysis

Novel probabilistic model for searching most probable point in structural reliability analysis

A hybrid self-adaptive conjugate first order reliability method for robust structural reliability analysis

Applied Methods of Structural Reliability.

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Applied Methods of Structural Reliability.