### Problem Description and Proposed Method  
An important role in CBR is played by the procedure for evaluating the similarity of a situation with another, that is, determining Sim (.). A sign of similarity between situations can be their belonging to the same class, i.e., Sim (.) \(\in\{0,1\}\), with the value 1 when two situations belong to the same class, and the value 0 otherwise. However, for a detailed classification, especially at the beginning of the system operation, there may not be a sufficient amount of information. In addition, during operation, new situations may appear that go beyond the previously created classification, which will require retraining of the system. Therefore, in CBR, the classification is used only at a sufficiently high level to separate typical situations. More detailing within types is performed using a set of practical cases that may appear during the system operation. This is what makes it possible, if necessary, to find in the knowledge base those cases that will most closely correspond to the current problem situation. As a consequence, some special similarity function [10] becomes more important than their belonging to one or another class for evaluating the similarity of the situations among themselves in CBR. The function value will show the similarity of situations among themselves. Calculation of the similarity function can be performed based on metrics (weighted Euclidean, Manhattan metrics, etc. ), by calculating the distance \(\rho\) between situations in the attribute space that describe these situations [6]. Then, Sim (.) \(=-\rho\) or Sim (.) \(=(1-\rho)\) in the case of normalization of distance values. This method has shown its effectiveness in many machine learning problems and in case-based inference problems when relatively simple and homogeneous objects are considered. On a complex object case, it is necessary to take into account the states of its various elements and the connections between them, which are described by many quantitative and categorical parameters. An attempt to compare situations in the multidimensional attributive space of a complex object faces some problems:  
* The need to create local similarity metrics and their aggregation into global metrics. * The need for expert judgment when ranking the importance of attributes. * The need to identify collisions, i.e., cases where the difference in situations in one attribute or local metrics can be compensated for by their similarity in other attributes. The problems described are associated with time-consuming tasks that require expert intervention. At the same time, the more complex the object, the higher the labor intensity, and the higher the probability of error and collisions. This set of problems requires new ways to assess the proximity of situations where expert intervention is minimized, and thereby the accuracy and speed of decision support processes is increased. As a solution to the described problems, we consider the use of neural networks. Artificial intelligence will speed up the decision-making process, which is especially important in critical situations, eliminate the human factor, and reduce the labor intensity of processes. The purpose of this work is to develop and research neural network architecture to evaluate the similarity of situations on a complex technological object of urban infrastructure. The work is based on previous studies, where we define the situation on a complex object through a set of elements states and connections between them [11; 12]. The article is organized as follows. Section 2 provides a brief overview of studies on the possibility of using neural networks to evaluate the proximity of the situation. Further, a formalized concept of a complex technological object is introduced, with a definition and formal presentation of situations arising at a complex object. Then, the process of forming a training data set is described.