This article describes a developed method for automatically optimizing the parameters of an intelligent controller based on an adaptive genetic algorithm. The key goal of this development is to improve the mechanism for generating an intelligent controller rule base through multiparameter optimization. The genetic algorithm is used to eliminate linguistic uncertainty in the design of control systems based on intelligent controllers. A unique algorithm is proposed that implements a comprehensive optimization procedure structured in three sequential stages: identifying optimal control system parameters, optimizing the structure of the intelligent controller rule base, simulating the automatic generation process, and then optimizing the intelligent controller parameters. Implementation of this approach optimizes the weights of fuzzy logic rules and the centers of the membership functions of linguistic variables.

Keywords: intelligent controller, optimization, genetic algorithm, uncertainty, term set

The paper proposes a method for automatic classification of roads based on the use of a convolutional neural network Mask-R-CNN. The developed technique makes it possible to automate the task of categorizing roads, which is fundamental in the redistribution of traffic flows, since knowledge of the category of the road allows you to determine its maximum capacity. The article contains a description of the stages of training a neural network, as well as the results obtained when using it. The method of automatic road classification proposed in the paper showed good results both in classifying roads based on satellite images and in classifying roads based on photographs of road sections. When expanding the test set, the number of classes of recognized roads can be increased to match the categories of roads according to SP 34.13330.2021. In addition, this technique (in terms of segmenting objects in photographs) can be used to control the quality of the roadway.

Keywords: road categories, convolutional neural networks, satellite imagery, image segmentation, Mask R-CNN, image recognition, computer vision

The development of a methodology for the formation of an estimate of a construction object on the basis of its information model is considered, taking into account the state of development of software systems for BIM modeling and the peculiarities of regulatory regulation of the construction of construction projects in Russia. The factors that limit the possibility of drawing up estimate documentation for the information model have been identified. Taking into account the identified limitations, a set of operations necessary for the formation of an estimate for a construction object based on its information model is presented.

Keywords: BIM, 5D BIM-model, estimate documentation, civil and industrial construction

The article proposes a method of automatic recognition of the type of building for an environmental monitoring system. based on convolutional neural networks. To train the neural network, the Keras library was chosen, containing numerous implementations of the main components of neural networks, such as layers, target and transfer functions, optimizers, and many tools to simplify working with images and text. The processes of network implementation using the Google Colab cloud platform, the preparation of a training set, the training of a constructed neural network, and its testing during training are described. The result of this work is a convolutional neural network model, capable of determining with accuracy of the order of 90-92 percent what type of buildings is shown on the cartographic image, which allows us to automate this process and use it as a subsystem for the environmental monitoring system of atmospheric air.

Keywords: environmental monitoring system for air, building type recognition, convolutional neural networks, machine learning, computer vision