graduate student
Russian Federation
Russian Federation
VAC 2.1.1 Строительные конструкции, здания и сооружения (Технические науки)
VAC 2.1.2 Основания и фундаменты, подземные сооружения (Технические науки)
VAC 2.1.3 Теплоснабжение, вентиляция, кондиционирование воздуха, газоснабжение и освещение (Технические науки)
VAC 2.1.5 Строительные материалы и изделия (Технические науки)
VAC 2.1.6 Гидротехническое строительство, гидравлика и инженерная гидрология (Технические науки)
VAC 2.1.7 Технология и организация строительства (Технические науки)
VAC 2.1.8 Проектирование и строительство дорог, метрополитенов, аэродромов, мостов и транспортных тоннелей (Технические науки)
VAC 2.1.9 Строительная механика (Технические науки)
VAC 2.1.10 Экологическая безопасность строительства и городского хозяйства (Технические науки)
VAC 2.1.11 Теория и история архитектуры, реставрация и реконструкция историко-архитектурного наследия (Технические науки)
VAC 2.1.12 Архитектура зданий и сооружений. Творческие концепции архитектурной деятельности (Технические науки)
VAC 2.1.13 Градостроительство, планировка сельских населенных пунктов (Технические науки)
VAC 2.1.15 Безопасность объектов строительства (Технические науки)
UDK 69 Строительство. Строительные материалы. Строительно-монтажные работы
UDK 004.94 Компьютерное моделирование
UDK 004 Информационные технологии. Компьютерные технологии. Теория вычислительных машин и систем
UDK 004.89 Прикладные системы искусственного интеллекта. Интеллектуальные системы, обладающие знаниями
GRNTI 67.01 Общие вопросы строительства
BBK 308 Монтаж, эксплуатация, ремонт машин и промышленного оборудования
In the article, the authors discuss the potential of digital transformation in the operation of buildings and structures. They propose a shift from a reactive to a preventive approach, which could reduce costs and improve efficiency during the service life of the buildings. To achieve this, they suggest using modern information technologies for detecting defects and controlling their spread. The article emphasizes the importance of collecting real-time data about buildings using digital twins of buildings. This information is collected from information models and sensors installed at the site. Instead of conducting regular inspections, it is suggested to use photo and video systems with artificial intelligence to detect defects in individual building elements. Artificial intelligence can also analyze all this data and predict the future condition of the building. Analyzing data from sensors and identifying defects based on survey information are two different tasks that require different types of neural networks. The training of these networks can be done using different methods, depending on the specific needs of the task. Based on this information, the authors propose a unified building management system that uses digital twins and artificial intelligence for various purposes. A key part of this system is continuous monitoring of building conditions and making predictions about future events. In the conclusion, the authors discuss the impact of their proposed system on building operations and structures. They assess how it can improve efficiency and reduce maintenance costs.
digital transformation, artificial intelligence, digital twin, operation of buildings and structures, information modeling technologies, machine learning, inspection of buildings and structures
1. Sun L. et al. Review of Bridge Structural Health Monitoring Aided by Big Data and Artificial Intelligence / Sun L., Shang Z., Xia Y., Bhowmick S., Nagarajaiah S. // Journal of Structural EngineeringArchive. 2020. Vol. 146. No 5. https://doi.org/10.1061/(ASCE)ST.1943-541X.0002535
2. Thiele C.-D. et al. A Digital Twin as a framework for a machine learning based predictive maintenance system / C.-D. Thiele, J. Brötzmann, T.-J. Huyeng, U. Rüppel, S. R. Lorenzen, H. Berthold, J. Schneider // ECPPM 2021 – eWork and eBusiness in Architecture, Engineering and Construction. 2021. https://doi.org/10.1201/9781003191476
3. Jones D. et al. Characterising the Digital Twin: A systematic literature review / Jones D., Snider C., Nassehi A., Yon J., Hicks B. // CIRP Journal of Manufacturing Science and Technology. 2020. Vol. 29. Part A. Pp. 36-52. https://doi.org/10.1016/j.cirpj.2020.02.002
4. Qiuchen Lu V. et al. Developing a Dynamic Digital Twin at a Building Level: using Cambridge Campus as Case Study / Qiuchen Lu V., Parlikad A.K., Woodall P., Ranasinghe G.D., Heaton J. // Journal of Management in Engineering. 2020. Vol. 36. No 3. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000763
5. Musella C. et al. BIM & AI: advanced technologies for the digitalisation of seismic damages in masonry buildings / C. Musella, M. Serra, C. Menna, D. Asprone // Conference: International fib Symposium on Conceptual Design Of Structures. Madrid. 2019.
6. Rafiu S. et al. Innovative changes in quantity surveying practice through BIM, big data, artificial intelligence and machine learning / Rafiu S., Young B.E., Clack Jamie, Adamu Z., Robinson. H. // Applied Science University Journal of Natural Science. 2020. Vol 4. Pp. 37–47.
7. Krausková V., Pifko H., Use of artificial intelligence in the field of sustainable architecture: current knowledge // Architecture Papers of the Faculty of Architecture and Design STU. 2021. Vol. 26. Pp 20–29. DOI:https://doi.org/10.2478/alfa-2021-0004
8. Petrova E. et al. In Search of Sustainable Design Patterns: Combining Data Mining and Semantic Data Modelling on Disparate Building Data / Petrova E., Pauwels P., Svidt K., Jensen R. L. // Advances in Informatics and Computing in Civil and Construction Engineering. 2018. Chapter 3. Pp. 19-26. DOI:https://doi.org/10.1007/978-3-030-00220-6_3
9. Adio-Moses D., Asaolu O.S., Artificial intelligence for sustainable development of intelligent buildings // Proceedings of the 9th CIDB Postgraduate Conference. University of Cape Town, South Africa. 2016.
10. Malekloo A. et al. Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights / Malekloo A., Ozer E., AlHamaydeh M., Girolami M. // Structural Health Monitoring. 2022. Vol 21. Pp. 1906–1955. https://doi.org/10.1177/14759217211036880
11. Renjiang Wu. Application of AI in construction // Proceedings of the 2023 International Conference on Software Engineering and Machine Learning. 2023. Vol. 8. Pp. 98-102. DOI:https://doi.org/10.54254/2755-2721/8/20230090
12. David Bassir et al. Application of artificial intelligence and machine learning for BIM:review / David Bassir, Hugo Lodge, Haochen Chang, Jüri Majak, Gongfa Chen. // Int. J. Simul. Multidisci. Des. Optim. 2023. Vol. 14: 5. https://doi.org/10.1051/smdo/2023005
13. Jiqiao Zhang et al. Sensitivity analyses of structural damage indicators and experimental validations / Jiqiao Zhang, Zhiqiang Teng, Xiaojian Xu, Gongfa Chen, David Bassir // Journal of Applied and Computational Mechanics. — 2020 — pp 798–810. DOI:https://doi.org/10.22055/JACM.2020.35529.2676
14. Zulkifley Hamid et al. Optimization assisted load tracing via hybrid ant colony algorithm for deregulated power system / Zulkifley Hamid, Professor Dr. Ismail Musirin, M.N.A. Rahim, Nor Azwan Mohamed Kamari // WSEAS Transactions Power Systems. — 2012 — vol 7 — pp 145–158. E-ISSN: 2224-350X
15. Mina Sadat Orooje, Mohammad Mehdi Latifi. A review of embedding artificial intelligence in internet of things and building information modelling for healthcare facility maintenance management // Energy and Environment Research. — 2021 — vol 11 — No 2. DOI:https://doi.org/10.5539/eer.v11n2p31
16. Fiorucci M. et al. Machine Learning for Cultural Heritage: A Survey / Fiorucci M., Khoroshiltseva M., Pontil M., Traviglia A., Del Bue A., James S. // Pattern Recognition Letters. — 2020 — vol 133 — pp 102–108. DOI:https://doi.org/10.1016/j.patrec.2020.02.017
17. Pierdicca, R. et al. Point Cloud Semantic Segmentation Using a Deep Learning Framework for Cultural Heritage / Pierdicca R., Paolanti M., Matrone F., Martini M., Morbidoni C., Malinverni E.S., Frontoni E., Lingua A.M. // Remote Sensing. — 2020 — vol 12 — No 6, 1005. DOI:https://doi.org/10.3390/rs12061005
18. Paumard M.-M., Picard D., Tabia H. Deepzzle: Solving Visual Jigsaw Puzzles with Deep Learning and Shortest Path Optimization // IEEE Transactions on Image Processing. — 2020 — vol 29 — pp 3569-3581. doi:https://doi.org/10.1109/TIP.2019.2963378
19. Ibrahim Y., Nagy B., Benedek C. Deep Learning-Based Masonry Wall Image Analysis // Remote Sensing. — 2020 — vol 12 — No 23. DOI:https://doi.org/10.3390/rs12233918
20. Li Jianchun et al. Damage identification in civil engineering structures utilising PCA-compressed residual frequency response functions and neural network ensembles / Li Jianchun, Dackermann Ulrike, Xu You-Lin, Samali Bijan // Structural Control and Health Monitoring. — 2011 — vol 18 —No 2 — pp 207–226. DOI:https://doi.org/10.1002/stc.369
21. Lin M. et al.Transfer learning with attributes for improving the landslide spatial prediction performance in sample-scarce area based on variational autoencoder generative adversarial network / Lin M., Teng S., Chen G., Bassir D. // Land. — 2023 — vol 12 — No 3:525. DOI:https://doi.org/10.3390/land12030525
22. Chen G. et al. A bridge vibration measurement method by UAVs based on CNNs and Bayesian optimization / Chen G., Yan Z., Teng S., Cui F., Bassir D. // Journal of Applied and Computational Mechanics. — 2023 — vol 9 — No 3 — pp 1–14. DOI:https://doi.org/10.22055/jacm.2022.41858.3823
23. Chen G. et al. An inversion algorithm for the dynamic modulus of concrete pavement structures based on a convolutional neural network / Chen G., Chen X., Yang L., Han Z., Bassir D. // Applied Sciences. — 2023 — vol 13 — No 2:1192. DOI:https://doi.org/10.3390/app13021192
24. Teng S. et al. Structural damage detection based on convolutional neural networks and population of bridges / Teng S., Chen X., Chen G., Cheng L., Bassir D. // Measurement. — 2022 — vol 202: 111747 — ISSN 0263-2241. DOI:https://doi.org/10.1016/j.measurement.2022.111747
