## 1 Introduction
There are also combined models that aim to reduce deviations from the empirical data that occur in simplified models. Hybrid models were developed by [PERSON] and [PERSON], [PERSON], [PERSON] and others. These models are also the most acceptable models for estimating the corrosion rate, and, as such, are presented in Figure 1.  
Considering the above, numerous studies and researches conducted on bulk carriers and tankers were aimed at analysing the corrosion of ship structural elements and assessing the speed of corrosion development in different environments for different types and sizes of ships. In this sense, the most significant studies are [PERSON] et al. [12], [PERSON] et al. [13], [PERSON] [14], [PERSON] [15], TSCF [16; 17] and [PERSON] et al. [18]. The last decade has been followed by numerous studies of corrosion models, the most significant of which have been published by [PERSON] and [PERSON] [19], [PERSON] [20], [PERSON] and [PERSON] models [21], the [PERSON] and Thayamballi model [4; 22; 23; 24], [PERSON] and [PERSON]'s model [25], [PERSON] et al. [26], [PERSON] et al. [27; 28]. While interpreting the corrosion development process (whether or not they take into account the effectiveness of the protective coating) usual corrosion models are developed as conventional models that assume that the corrosion rate has a constant value. [PERSON] [21] and [PERSON] et al. [12], developed a linear, and linear/bilinear model, respectively. [PERSON] et al. [13], [PERSON] and [PERSON] [21], [PERSON] et al. [3; 4; 5; 6] developed a three-phase model, while [PERSON] developed a four-phase model [29; 30] and a five phases model [31]. These phases describe the external conditions in which corrosion develops, as well as the conditions of the resulting corrosion. Special attention is paid to the creation of unique and improved corrosion models based on the improvement of existing models, such as those in [PERSON] and [PERSON] [25; 32], [PERSON] [33], and [PERSON] and [PERSON] [34]. It is obvious that despite extensive databases and numerous models of general and pitting corrosion, there is a significant dispersion of the results of previous research. These studies cannot be considered as guidelines nor could credibly become universal models for estimating corrosion rates. The specific conditions that characterise the exploitation of metallic materials determine the degree of corrosion for each metal structure separately, and each of the presented models can only serve as a guide that directs the future user to pay more attention to certain materials during the exploitation cycle. In the paper of [PERSON] et al. [36], a statistical approach is given to investigate the corrosion rate of a CuAlNi Shape Memory Alloy. The average corrosion depths of CuAlNi alloy metal samples exposed to different seawater environments were used to determine the approximate value of the corrosion rate. In this statistical analysis, the corrosion rate was observed as a random variable represented by a linear corrosion model, based on the assumption that corrosion affects metal samples immediately upon exposure to the marine environment. Using statistical tests, the best two-parameter continuous distributions were determined, to represent the corrosion rate adequately as a random variable. This work is organised as follows. In Section 2, the motivation of this research is presented, which is based on the analysis of the corrosive behaviour of two different NiTi alloys. This section provides an insight into the characteristics of the considered alloys from the point of view of the production process, their microstructure and physical characteristics. In addition, the methods on the basis of which empirical databases were formed are given, as well as their statistical descriptions. As the research in this paper relies on existing models of corrosion known in the literature, in this chapter a brief description of the linear corrosion model is given, which is the basis for the probabilistic model developed for the observed NiTi alloys.