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All issues Volume 410 (2023) E3S Web of Conf., 410 (2023) 02031 References
Open Access
Issue
E3S Web of Conf.
Volume 410, 2023
XXVI International Scientific Conference “Construction the Formation of Living Environment” (FORM-2023)
Article Number 02031
Number of page(s) 8
Section Reliability of Buildings and Constructions
DOI https://doi.org/10.1051/e3sconf/202341002031
Published online 09 August 2023
  1. Kim D. A. Actual problems of technical inspection of buildings falling under the zone of influence of construction and reconstruction works / D. A. Kim // Problems of science and education. - 2019. - No. 11 (57). – P. 26–30. [Google Scholar]
  2. Ndayiragidzhe I. V. Artificial neural networks as a tool for optimizing production processes in construction / I. V. Ndayiragidzhe, A. A. Lapidus // Technology and organization of construction production. - 2018. - No. 4. - P. 3-6. [Google Scholar]
  3. Naumov A.E. Improving the technology of construction and technical expertise using the hardware-software complex of automated flaw detection / A.E. Naumov, D.A. Yudin, A.V. Dolzhenko // Bulletin of the Belgorod State Technological University. V. G. Shukhov. - 2019. - No. 4. - P. 61-69. [Google Scholar]
  4. Sikorsky O.S. Overview of convolutional neural networks for the problem of image classification / O.S. Sikorsky // New information technologies in automated systems. - 2017. - No. 3. - P. 15-23. [Google Scholar]
  5. Sosnin A. S., Suslova I. A. Activation functions of a neural network: sigmoid, linear, stepped, ReLu, tahn // Nauka. Informatization. Technologies. Education. - Yekaterinburg:, 2019. - S. 237-246. [Google Scholar]
  6. A Two-Stage Crack Detection Method for Concrete Bridges Using Convolutional Neural Networks. / Y. Li, W. Zhao, X. Zhang, Q. Zhou // IEICE Transactions on Information and Systems. – 2018. – № E101.D (12). – С. 3249-3252. [Google Scholar]
  7. Automatic Pixel-Level Crack Detection and Measurement Using Fully Convolutional Network / X. Yang, H. Li, X. Yu [и др.] // Computer-Aided Civil and Infrastructure Engineering. – 2018. – № 33 (4). – С. 1090–1109. [CrossRef] [Google Scholar]
  8. Automated Pixel-Level Pavement Crack Detection on 3D Asphalt Surfaces Using a Deep-Learning Network / A. Zhang, K. Wang, E. Yang [и др.] // Computer-Aided Civil and Infrastructure Engineering. – 2017. – № 32 (5). – С. 805–819. [CrossRef] [Google Scholar]
  9. Cheng J. Automated Detection of Sewer Pipe Defects in Closed-Circuit Television Images Using Deep Learning Techniques / J. Cheng, M. Wang // Automation in Construction. – 2018. – № 95. – С. 155-171. [CrossRef] [Google Scholar]
  10. Dorafshan S. Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete / S. Dorafshan, R.J. Thomas, M. Maguire // Construction and Building Materials. – 2018. – № 186. – С. 1031-1045. [CrossRef] [Google Scholar]
  11. Howard J. Unmanned aerial vehicles in construction and worker safety / J. Howard, V. Murashov, C. M. Branche // American Journal of Industrial Medicine. – 2018. – № 61. – С. 3-10. [CrossRef] [PubMed] [Google Scholar]
  12. Kim B. Automated Vision-Based Detection of Cracks on Concrete Surfaces Using a Deep Learning Technique / B. Kim, S. Cho // Sensors. – 2018. – № 18 (10). – С. 1-18. [Google Scholar]
  13. Tamrazyan A.G. Methodology of analysis and assessment of the reliability of the state and forecasting the service life of reinforced concrete structures // Reinforced concrete structures. 2023. V. 1. No. 1. S. 5–18. [Google Scholar]
  14. Kabantsev O.V., Karlin A.V. Features of the structure of the concrete element of the structure, made by additive technology // Reinforced concrete structures. 2023. V. 1. No. 1. S. 55–63. [Google Scholar]
  15. Trekin N.N., Kodysh E.N., Terekhov I.A. Improving the regulatory framework for the standardization of prefabricated reinforced concrete structures // Reinforced concrete structures. 2023. V. 1. No. 1. S. 64–71. [Google Scholar]
  16. A. G. Tamrazyan, D. S. Dekhterev. Assessing the impact of structural parameters on the reliability of a platform joint of panel buildings by the method of statistical modeling. Industrial and civil engineering, 2016, No. 7, pp. 5-10. [Google Scholar]
  17. Methodology for assessing the technical condition of buildings and structures using the reliability criterion / Denis S. Dekhterev, Ashot G. Tamrazyan // 13th International Conference on Contemporary Problems of Architecture and Construction (ICCPAC), Yerevan, Armenia, 2021 - с 115-119. [Google Scholar]
  18. On the assessment of the durability of reinforced concrete structures based on the survey of the engineering and technical condition of buildings / D.S. Dekhterev // Prospects of Science, Tambov, 2022 - pp. 78-81. [Google Scholar]
  19. A.G. Tamrazyan, D.S. Dekhterev. Investigation of the reliability of the horizontal monolithic joint of prefabricated wall elements of panel buildings by the method of statistical modeling. Innovations and investments, 2017, No. 11, pp. 170-174. [Google Scholar]
  20. D.S. Dekhterev. Analytical assessment of the weight of the influence of structural parameters of the joints of columns of frame buildings on the reliability of the connection. Construction and reconstruction, 2019, No.2, pp. 11-19. [Google Scholar]

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