The study deals with stability and dynamic problems in bar structures using a probabilistic approach. Structural design parameters are defined as deterministic values and also as random variables, which are not correlated. The criterion of structural failure is expressed by the condition of non-exceeding the admissible load multiplier and condition of non-exceeding the admissible vertical displacement. The Hasofer-Lind index was used as a reliability measure. The primary research tool is the FORM method. In order to verify the correctness of the calculations Monte Carlo and Importance Sampling methods were used. The sensitivity of the reliability index to the random variables was defined. The limit state function is not an explicit function of random variables. This dependence was determined using a numerical procedure, e.g. the finite element methods. The paper aims to present the communication between the STAND reliability analysis program and the KRATA and MES3D external FE programs.

The paper deals with the variability of mechanical properties of EPSTAL steel rods produced in Polish steelworks, i.e. yield stress Re, tensile strength Rm, and elongation Agt. Our study is based on fundamental engineering static room-temperature tensile tests for large series specimens which have been made by manufacturers as the part of a factory quality control. Statistical analysis of these results shows that the stressstrain relationship of steel tensile tests should be described by a one-dimensional stochastic process, and three the most important mechanical parameters, i.e. the yield stress, tensile strength, and elongation by random variables. Based on the statistical elaboration of experimental data, it was found that the yield stress and tensile strength of steel rods produced in the years 2016-2017 had the coefficients of variation of less than 3%, and there is a reasonable basis for the manufacturer to increase the characteristic value of EPSTAL steel rods yield stress by a few percentages.

Missing data in test result tables can significantly impact the analysis quality, especially in relation to technical sciences, where the mechanism generating missing data is often non-random, and their presence depends on the non-observed part of studied variables. In such cases, the application of an inappropriate method for dealing with missing data will lead to bias in the estimated distribution parameters.
The article presents a relatively simple method to implement in dealing with missing data generated as a result of the MNAR mechanism, which utilizes the censored random variable. This procedure does not modify the variable distribution form, which is why it ensures objective and efficient estimation of distribution parameters within studies affected by certain restrictions of technical or physical nature (censored distribution), with a relatively low workload. Furthermore, it does not require the application of specialized software. A prerequisite for using this method is the knowledge of the frequency and cause of missing data.
The method for estimating the random variable censored distribution parameters was shown based on the example of studying the leachability of selected heavy metals from a hardening slurry. The analysis results were compared with classical methods for dealing with missing data, such as, ignoring missing data observations (listwise or pairwise deletion), single imputation and stochastic regressive imputation.