Integration of information models between different software systems is an urgent task. There are a large number of CAD systems that cover more than 90% of the design tasks, but the mechanism for transferring information between these programs has not yet been worked out. Despite the use of common data integration formats (such as IFC, XML, DXF, DWG, PDF), CAD model elements are represented using various indicators and characteristics. This leads to a partial loss of information on objects. Integration of the model in an incomplete form and filling in the missing parameters manually takes a lot of time and is not effective. To solve this problem, research was carried out, the result of which was the generalization and structuring of the parameters of the elements of information models of building objects. This allows you to solve the problem of heterogeneity of information about the model coming from different sources. Cite this Article: Kateryna I. Kyivska, Svitlana V. Tsiutsiura, Mikola I. Tsiutsiura, Olena V. Kryvoruchko Andrii V. Yerukaiev and Vladyslav V. Hots, a Study of the Figure.3 Types of elements in the format of presentation of modelsDepending on the format of the presentation of the model, the elements will have a different set of parameters. So, for the 2D model, graphical parameters (coordinates, dimensions) are relevant, for 3D+ models -physical parameters of the constructive elements (material, section, mass, level of location, load on the element, mode of interaction with other elements: node connection, hinge connection, hard connection, and their means of processing: trimming, crossing, reaching, etc.), for the calculation model, the parameters of finite elements and nodes (boundary conditions, estimated coefficients, etc.).
Аспірант кафедри інформаційних технологій, orcid.org/0000-0003-4713-7568 Київський національний університет будівництва і архітектури, Київ АНАЛІЗ ЗАСТОСУВАННЯ ШТУЧНОГО ІНТЕЛЕКТУ В BIM-ТЕХНОЛОГІЯХ Анотація. Штучний інтелект швидко поширюється і наразі проникає в будівельну галузь. Інформаційне моделювання будівель (BIM) вважається одним із ключових елементів у сфері архітектури, інжинірингу та будівництва (АЕС) з об'ємом ринку $ 8 млрд до 2020 року в різних сегментах, таких як комерційний, освітній, житловий, медичний, промисловий, розважальний і спортивний. BIM -це відносно нова технологія в галузі, яка зазвичай повільно запроваджує зміни. Отже, багато вчених впевнені, що BIM буде рости експоненціально через розвиток цифрових технологій, таких як мобільний зв'язок, ІоT, Big Data, Data Sсіence, машинне навчання і штучний інтелект. Пряма інтеграція технології машинного навчання в BIM може сприяти в різних сферах, таких як: ідентифікація об'єкта за замовчуванням з попередніх аналогічних проєктів; надлишкове видалення інформації в процесі навчання без учителя. Напівконтрольоване або контрольоване навчання може модернізувати більш ранні дані, що не належать до BIM, до складних проєктів з підтримкою BIM. Агенти з посиленого навчання можуть допомогти в управлінні і обслуговуванні в режимі онлайн. Навіть готова розробка машинного навчання, така як розпізнавання мови, виявлення об'єкта, ідентифікація об'єкта або опрацювання природної мови з використанням технології машинного навчання, може використовуватися для прямого відновлення моделі замість схильного помилок ручного введення. Як і будь-який новий процес, зміна потребує часу, особливо в такій галузі, як будівництво. Однак спрощений та економічно ефективний процес проєктування BIM пропонує доведене зниження витрат та покращення якості, чого можна легко досягти за допомогою штучного інтелекту та машинного навчання.
The object of research is the process of using information technology in the construction industry. One of the most problematic areas is increasing the efficiency of the construction industry through the introduction of digital technologies. The research carried out is based on the application of an approach that is implemented using artificial intelligence. The study used machine learning and fuzzy logic methods to mark visual data and analyze it for potential threats, as well as to reduce all possible risks. The main feature of this approach is that using machine learning technology, it is possible to reduce the risks of a project before they affect its profit. So, using artificial intelligence in combination with BIM technologies, it is possible to predict work on construction projects based on real-time data, past activities and other factors in such a way as to optimize construction processes. The benefits to be gained from implementing digital processes will become even more evident in future projects as AI continues to analyze company data. This is due to the fact that the proposed approach using fuzzy logic has a number of features, in particular, the more information machine learning algorithms process, the more complex they become. As a result, they provide even more useful information and allow to make even better decisions. This provides an opportunity to minimize risks and efficiently allocate resources when working on projects. Compared to conventional information technology, artificial intelligence can be used to build a knowledge-based security management system and combine statistical probabilities to help mitigate security risks in construction projects.
The implementation of the strategic goal of creating a competitive economy of Ukraine and ensuring a high standard of living for the citizens of Ukraine requires the active use of the scientific potential of the state in the creation of progressive information technologies for managing the development of higher education institutions. The globalization of the development of the educational space is associated with the active application of the value-competency approach in the era of the transition to the knowledge economy, the life cycles of updating educational programs are shortened, which makes the programs mobile, technologies, means and mechanisms, management systems, etc. are quickly and significantly complicated. In these conditions, the requirements for the competence of specialists are formed intuitively. At the same time, there is a high turnover of the number of entrants as a result of negative demographic and economic processes. Global trends create challenges that accelerate the development of the educational space and create new challenges for higher education. One of these challenges is the transition to project management in educational environments, which requires the creation of innovative management methodologies that take into account the specifics of activities aimed at training specialists. These methodologies should ensure the activity of higher education institutions in the educational process, the scientific work of educational institutions with innovative management tools.
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