inbureИзвестия вузов. Инвестиции. Строительство. НедвижимостьIzvestiya vuzov. Investitsii. Stroitelstvo. Nedvizhimost2227-29172500-154XIrkutsk National Research Technical University10.21285/2227-2917-2025-3-586-599SCUGCIinbure-359Research ArticleАРХИТЕКТУРА. ГРАДОСТРОИТЕЛЬСТВО. ДИЗАЙНARCHITECTURE. URBAN CONSTRUCTION. DESIGNПрименение искусственного интеллекта и машинного обучения для BIMApplication of artificial intelligence and machine learning for BIMПичуговП. A.PichugovP. A.

Пичугов Павел Алексеевич, аспирант

454080, г. Челябинск, пр. Ленина, 76

Pavel A. Pichugov, Postgraduate Student

76 Lenin Ave., Chelyabinsk 454080

pichugovp@yandex.ruhttps://orcid.org/0000-0001-9405-2079ШабиевС. Г.ShabievS. G.

Шабиев Салават Галиевич, доктор архитектуры, профессор, заведующий кафедрой архитектуры

454080, г. Челябинск, пр. Ленина, 76

Author ID: 476175

Salavat G. Shabiev, Doctor of Architecture, Professor, Head of the Department of Architecture

76 Lenin Ave., Chelyabinsk 454080

Author ID: 476175

shabievsg@susu.ruЮжно-Уральский государственный университетРоссияSouth Ural State UniversityRussian Federation202513102025153586599Copyright © Пичугов П.A., Шабиев С.Г., 20252025Пичугов П.A., Шабиев С.Г.Pichugov P.A., Shabiev S.G.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://inbure.elpub.ru/jour/article/view/359

Контроль качества является неотъемлемым компонентом процессов информационного моделирования зданий в архитектурном проектировании. На каждом этапе жизненного цикла объекта требуется систематический сбор и мониторинг ключевых показателей. Технология BIM, основанная на интенсивном использовании данных, предполагает применение сложных вычислительных методов, таких как обработка изображений и анализ больших массивов информации. В этом контексте искусственный интеллект и машинное обучение доказали свою эффективность в автоматизации задач и извлечении ценных инсайтов как в России, так и за рубежом. Эти технологии также позволяют прогнозировать необходимость технического обслуживания и контроля качества с высокой точностью, определяя оптимальные временные и пространственные параметры. В данной статье проводится анализ современных подходов к интеграции искусственного интеллекта и машинного обучения в архитектурном проектировании, а также обсуждаются перспективы и вызовы, связанные с внедрением этих технологий в архитектурное проектирование, строительство и ландшафтный дизайн. Цель исследования — сформировать комплексное представление о текущих потребностях архитектуры и отрасли строительства и влиянии искусственного интеллекта и машинного обучения на их развитие, а также определить направления для дальнейших научных изысканий. Особое внимание уделяется оперативным системам, способным решать сложные задачи и обучаться на основе данных, что обеспечивает высокую точность в выявлении закономерностей и прогнозировании жизненного цикла объектов, особенно при обработке значительных объемов информации.

 

Quality control is an integral component of building information modeling processes in architectural design. At each stage of the facility's life cycle, systematic collection and monitoring of key indicators is required. BIM technology, based on the intensive use of data, involves the use of complex computational methods such as image processing and analysis of large amounts of information. In this context, artificial intelligence and machine learning have proven effective in automating tasks and extracting valuable insights both in Russia and abroad. These technologies also make it possible to predict the need for maintenance and quality control with high accuracy, determining optimal time and spatial parameters. This article analyzes modern approaches to the integration of artificial intelligence and machine learning in architectural design, and discusses the prospects and challenges associated with the implementation of these technologies in architectural design, construction and landscape design. The purpose of the study is to form a comprehensive understanding of the current needs of architecture and the construction industry and the impact of artificial intelligence and machine learning on their development, as well as to identify areas for further scientific research. Special attention is paid to operational systems capable of solving complex tasks and learning from data, which ensures high accuracy in identifying patterns and predicting the life cycle of objects, especially when processing significant amounts of information.

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The authors declare that there are no conflicts of interest present.