Increasing the quality of organizational and technological solutions in construction is one of the main tasks facing the construction industry. The actual methods of solving the problem of setting organizational and technological design are directly related to the integration of specialized software in the planning and design of construction projects, especially for unique, complex projects, projects implemented in tight time and construction sites in the current urban development. The existing need to process a significant amount of information at short intervals and to link design decisions to the dynamic environment of the construction site is not an easy task, but a realizable one. Within the framework of the research work carried out at the department of MGSU, the expediency of applying operational assessments of the parameters of organizational and technological solutions based on mathematical methods has been established. The proposed method for forecasting and evaluating the integral parameters of design solutions is based on a neural network model, the method used involves the formation of a training matrix comprising key indicators of implemented (pilot) ones.

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With the development of information technologies the popularity of access control systems and personal identification systems is growing. One of the most common methods of access control is biometric identification. Biometric identification is more reliable than traditional identification methods, such as login/password, card, PIN-code, etc. In recent years, special attention has been paid to biometric identification based on facial recognition in access control systems, due to sufficient accuracy, scalability and a wide range of applications: face recognition of intruders in public places, providing access control, etc. The purpose of this article is to design the architecture of the subsystem "Identifier" in the hardware-software complex face recognition. In article methods and models for recognition of the face on the image and in video stream are considered. As a base method the deep neural network is chosen, the basic advantages and lacks of the chosen approach are considered. Special attention is paid to the description of architecture and scenario of work of subsystem "Identifier" of neural network identification software and hardware complex, which implements face recognition in real time from the incoming video stream of IP and USB cameras. Improvements of the traditional algorithm of face recognition using the k-neighbor method are described in detail. The results of the conducted experiments including the influence of head rotation angle on the accuracy of identification are given, and conclusions about the applicability of this method in security systems are made. On the basis of carried out researches the software and hardware complex of biometric identification on the basis of neural network recognition of faces, for the subsequent integration into the security system of the Moscow Technical University of Communications and Informatics (MTUCI) is created.

In the study of certain technological processes, students lack practice to a large extent therefore it is difficult to understand assimilation of a significant part of the educational material. As part of academic studies there is no opportunity to demonstrate the full technological cycle of construction and installation works. Multidimensional modelling tools, 4D models in particular, redound to demonstrate technological processes with high detail remotely from the construction site, 4D model tools allow to demonstrate how technological solutions can affect the duration of work and the quality of the realized objects. Similarly construction industry where the effect of the introduction of information technology is achieved as the reduction of term and cost of the project taking modern methods and tools in educational processes allow to improve the competence of students. The creation of detailed models with a developed work breakdown structure requires a significant amount of resources, including the time of work of highly qualified design engineers. This article discusses issues of labour costs reducing for the creation of multidimensional models due to applying the cluster system of model formation.