Automating the Process of Assessing the Technical Condition of Exterior Walls of Brick Buildings Using Machine Learning
https://doi.org/10.21822/2073-6185-2025-52-3-61-70
Abstract
Objective. The aim of this study is to develop a program for determining the technical condition of buildings using machine learning. The objectives of the study include analyzing existing machine learning methods, writing program code, collecting a training dataset, determining the optimal ratio of training parameters, and determining the most accurate machine learning algorithm for the given parameters and input data.
Method. The study is based on methods and algorithms for diagnosing the condition of technical systems using machine learning technology.
Result. The results demonstrate that even with a limited amount of data, the program is capable of correctly and accurately determining technical condition categories, minimizing the risk of missing emergency situations, and also confirms the potential of using machine learning in construction diagnostics.
Conclusion. The significance of the obtained results for the construction industry is that the use of the developed program will increase the accuracy and speed of building inspections, which in turn will improve their safety.
About the Authors
S. A. Krylov
Perm National Research Polytechnic University
Russian Federation
Russian Federation
Sergey A. Krylov - Graduate Student, Department of Building Structures and Computational Mechanics.
29 Komsomolsky Ave., Perm 614990
G. G. Kashevarova
Perm National Research Polytechnic University
Russian Federation
Russian Federation
Galina G. Kashevarova - Dr. Sci.(Eng.), Prof., Corresponding Member of the Russian Academy of Architecture and Construction Sciences, Head of the Department of Building Structures and Computational Mechanics.
29 Komsomolsky Ave., Perm 614990
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Review
For citations:
Krylov S.A., Kashevarova G.G. Automating the Process of Assessing the Technical Condition of Exterior Walls of Brick Buildings Using Machine Learning. Herald of Dagestan State Technical University. Technical Sciences. 2025;52(3):61-70. (In Russ.) https://doi.org/10.21822/2073-6185-2025-52-3-61-70
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