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The computational assessment of system reliability of structures has remained a challenge in the field of reliability engineering. Calculation of the failure probability for a system is generally difficult even if the potential failure modes are known or can be identified, because available analytical methods require determination of the sensitivity of performance functions, information on mutual correlations among potential failure modes, and determination of design points. In the present paper, a method based on moment approximations is proposed for structural system reliability assessment that is applicable to both series and nonseries systems. The point estimate method is applied to evaluate the first few moments of the system performance function of a structure from which the momentbased reliability index and failure probability can be evaluated without Monte Carlo simulations. The procedure does not require the computation of derivatives, nor determination of the design point and computation of mutual correlations among failure modes; thus, it should be computationally effective for structural assessment of system reliability.

In structural reliability analysis, the uncertainties related to resistance and load are generally expressed as random variables that have known cumulative distribution functions. However, in practical applications, the cumulative distribution functions of some random variables may be unknown, and the probabilistic characteristics of these variables may be expressed using only statistical moments. In the present paper, in order to conduct structural reliability analysis without the exclusion of random variables having unknown distributions, the third-order polynomial normal transformation technique using the first four central moments is investigated, and an explicit fourth-moment standardization function is proposed. Using the proposed method, the normal transformation for independent random variables with unknown cumulative distribution functions can be realized without using the Rosenblatt transformation or Nataf transformation. Through the numerical examples presented, the proposed method is found to be sufficiently accurate in its inclusion of the independent random variables which have unknown cumulative distribution functions, in structural reliability analyses with minimal additional computational effort.