In many areas of the economy, including the construction industry, new IT technologies are being successfully introduced - Cloud computing, Big Data, Internet of Things etc. A significant place among these technologies is taken by the direction of “Machine Vision”. This technology allows to bring to a qualitatively different level a number of important applications in the construction industry and therefore deserves the close attention of specialists. For example, with the help of machine vision it is possible to consider and analyze the smallest defects invisible to the human eye (for example, when controlling the quality of an important element of construction structures - welds). The result is not affected by fatigue or inattention of the staff, you can work 24 hours, there are other advantages discussed in the article. The best effect is achieved with the complex application of new IT technologies. The most promising is the use of machine vision in combination with IoT and Big Data technologies. The paper explores the possibility of using machine vision in the construction industry as a stand-alone solution and in combination with other new IT technologies. The possibility of using unmanned aerial vehicles (drones) equipped with a machine vision system is being considered - for monitoring construction sites and surrounding areas.
This article studies the existing approaches to the analysis of the highest and best use, defines the specifics of the industry, and considers the main aspects of the analysis. A comprehensive methodology for the analysis of the highest and best use in the course of implementation of investment and construction projects for the purposes of tourism clusters infrastructure development is presented. The aim of the study is to improve the mechanism for the development of tourism clusters infrastructure based on the analysis of the highest and best use of investment and construction projects. The relevance of the article is substantiated by problems and underdevelopment of the tourist infrastructure during the creation and development of territorial clusters. In addition, constituent entities of the Russian Federation do not create favorable investment conditions in the course of construction of various objects of tourist use. The existing approaches to the analysis of the highest and best use of investment and construction projects need to be improved taking into account modern conditions, factors and industry specifics. The article offers an analysis of the current state of domestic tourism in Russia and abroad, identifies factors hindering the functioning and development of investment and construction projects for the purposes of tourism clusters infrastructure development, identifies factors that have a positive impact on the domestic tourism market. Measures are proposed to determine the highest and best use in the course of implementation of an investment and construction project for the tourism cluster of the North Caucasian recreational region and to calculate its effectiveness. An element of scientific novelty and the result of the study is the improvement of approaches to the analysis of the highest and best use of tourism clusters for infrastructure development. The practical significance lies in the application of an improved mechanism for the development of investment and construction projects for tourism clusters objects in Russia.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.