## 1 Introduction  
At all stages of the engineering structures creation process, from the concept through the design to execution, more or less imprecise parameters occur. To begin with, one can specify among them those related to the material from which the structure is made. These are, for example, the values of the strength of wooden elements depending on the humidity of the environment, stiffness of asphalt mixtures changing with their temperature or all other characteristics related to the irregular structure of material. Furthermore, the structure is exposed to all kinds of inaccuracies, resulting from assembly errors, a change in geometry resulting from rheological phenomena or differences between the actual structure and the adopted calculation model. One should also not forget about large changes in the value of loads over time, the characteristics of which are often unpredictable in the long run. Carrying out engineering calculations requires assuming the geometry of the object, its physical parameters, support method, load, etc. Usually, these data are adopted on the basis of design standards, available tables with parameters or the results of experimental tests. Then one solution is obtained, which is used in the design process. Such a solution can be identified with a single point in a space and may contain various information. However, the calculation parameters are specified with a certain degree of imprecision, which examples of reasons are mentioned in the first paragraph of the present section. Therefore, one deals with the uncertainty of the adopted parameters. They can have different, but of course not arbitrary, values. The solution is then not a point in a space, but a set of points which is called a solution set. Thus, obvious questions arise in the further course of discussion. What is this collection? What is its shape and range? There is no simple relationship between the uncertainty of parameters measured by percentage, for example, and the size and shape of the solution set. Precise determination of this set is difficult and requires the use of advanced mathematical tools. One possible analysis technique related to the theory of convex sets is presented in this paper. To orient the reader in the technique of proceeding, instructional examples were selected, in which it is possible to present the results in a graphical form. Complex shapes of solution sets obtained for simple tasks allow one to find out about the wealth of possible solutions that are possible to receive in large tasks. The wide range of possible occurrence of uncertainties in engineering structures implies a variety of methods that allow them to be included in computational models. In the world literature one can find many works regarding the consideration of uncertainty [1]. The most commonly implemented methods can be classified into two groups, namely probabilistic and non-probabilistic methods. In the first group uncertain parameters are treated as random values. For the description of uncertainty the knowledge of the reliable probability distribution is crucial [2]. The selection of distribution has a significant impact on the results, what was shown in the work [3], where a comparison of the results for the probability distributions of random variables most commonly used in practice, such as [PERSON], Gumbel, Frechet's distributions, was made. Likewise, random methods are implemented to extend the classic deterministic FEM, which results in the stochastic finite element method. This allows the analysis of problems of statics and dynamics with the use of finite elements with random parameters [4]. In the group of non-probabilistic methods, the most common assumption is that uncertain parameters are unknown-but-bonded. Such an assumption allows for obtaining more reliable results than when the task parameters are given as certain, as shown in [5] on the example of buckling analysis of reinforced concrete columns.