Naive Fault Trees for Safety Evaluations in Early Project Phase

Naive Fault Trees (NFT) aim to extend the application of Fault Trees (FT) and make them appealing for system designers in the early project life cycle. NFT use input intervals and values to estimate the frequency of a top event. This extension facilitates the assignment of failure probability to basic events when exact data is difficult to find, unavailable or even not existent. The formulation of the problem and results are presented in this paper through an application to a real-world example. ed from different candidates contain ample information, which might not appear evident at first sight. The complexity of the situation requires an intelligent extraction of the information from the data. An analysis tool IndEvawas developed to handle this complexity and provide an accurate, detailed and reliable evaluation of inspection systems and personnel. Besides the plain evaluation regarding the fulfilment of the qualification requirements, critical test flaws as well as test block sections, which are likely to cause false positive indications can be identified. Statistic results display the dependency of the system performance on various parameters and parameter combinations to provide a clear picture of the performance. Country-specific evaluation standards can be applied and compared, especially with regard to the continuous improvement of the qualification methodology

The inclusive and simplified forms of Bayesian interpolation for general and monotonic models using Gaussian and Generalized Beta distributions with application to Monte Carlo simulations

A recently developed Bayesian interpolation method (BI) and its application to safety assessment of a flood defense structure are described in this paper. We use a one-dimensional Bayesian Monte Carlo method (BMC) that has been proposed in (Rajabalinejad 2009) to develop a weighted logical dependence between neighboring points. The concept of global uncertainty is adequately explained and different uncertainty association models (UAMs) are presented for linking the local and global uncertainty. Based on the global uncertainty, a simplified approach is introduced. By applying the global uncertainty, we apply the Guassian error estimation to general models and the Generalized Beta (GB) distribution to monotonic models. Our main objective in this research is to simplify the newly developed BMC method and demonstrate that it can dramatically improve the simulation efficiency by using prior information from outcomes of the preceding simulations. We provide theory and numerical algorithms for the BI method geared to multi-dimensional problems, integrate it with a probabilistic finite element model, and apply the coupled models to the reliability assessment of a flood defense for the 17th Street Flood Wall system in New Orlean