A sampling method is developed for failure probability calculations which requires that the following assumptions are fullfilled: 1. (i) the limit state function, z, defining the combinations of values of the basic variables for which failure will occur, is known; 2. (ii) the basic variables are stochastically independent, and normally distributed, with mean value 0 and variance 1; 3. (iii) the reliability index, β, i.e. the distance from the origin in the k-dimensional basic variable space to the point (design point) on the failure surface which is closest to the origin, is known. The main idea is to restrict the sampling domain in the basic variable space, Ω, to the tail part of the joint distribution of the basic variables. The sampling domain is restricted to values outside the k-dimensional ß-sphere in Ω, since no failure values occur inside the ß-sphere. A random value is sampled by sampling a random direction in Ω and a random radius, R > ß. The R-sampling is performed by an appropriate variable transformation and use of the acceptance sampling technique. The method outlined is especially useful when the failure surface has a dangerous curvature, i.e. when the P f-value is significantly greater than the value calculated by first order reliability technique approximating the failure surface with the hyperplane tangential to the failure surface at the design point. A remarkable increase in efficiency is obtained compared to the simple Monte Carlo technique which includes the ß-sphere in the sampling domain. The number of simulations necessary in order to obtain the same accuracy as by the simple Monte Carlo technique is reduced by a factor of about 1 divided by the probability content outside the ß-sphere, ( 1 − Γ k(ß 2) ), where Γ k denotes the cumulative chisquare distribution with k degrees of freedom. Numerical examples are given for a k-dimensional hyperparaboloid with k = 4, 6, 8, 10. Reasonable results are obtained based on only 100 simulations. The corresponding number of simulations necessary to obtain the same accuracy by use of the simple Monte Carlo technique are in some of the cases more than one million.
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