The objective of the paper is to present methods and software for the efficient statistical, sensitivity and reliability assessment of engineering problems. Attention is given to small-sample techniques which have been developed for the analysis of computationally intensive problems. The paper shows the possibility of “randomizing” computationally intensive problems in the manner of the Monte Carlo type of simulation. In order to keep the number of required simulations at an acceptable level, Latin Hypercube Sampling is utilized. The technique is used for both random variables and random fields. Sensitivity analysis is based on non-parametric rank-order correlation coefficients. Statistical correlation is imposed by the stochastic optimization technique – simulated annealing. A hierarchical sampling approach has been developed for the extension of the sample size in Latin Hypercube Sampling, enabling the addition of simulations to a current sample set while maintaining the desired correlation structure. The paper continues with a brief description of the user-friendly implementation of the theory within FReET commercial multipurpose reliability software. FReET-D software is capable of performing degradation modeling, in which a large number of reinforced concrete degradation models can be utilized under the main FReET software engine. Some of the interesting applications of the software are referenced in the paper. 
FReET: Software for the statistical and reliability analysis of engineering problems and FReET-D: Degradation module