Assessment of changes in the technical condition of damaged multi-story buildings by using artificial intelligence
DOI:
https://doi.org/10.32347/2411-4049.2025.2.185-198Keywords:
damaged multi-story buildings, technical condition, mathematical modeling, neural networks, risk of progressive collapseAbstract
The article presents the results of the analysis and prospects for applying information technologies to select effective organizational, technological, and technical solutions for eliminating the emergency destruction of multi-story buildings damaged as a result of Russian aggression. Information and mathematical modeling is considered a key tool for assessment and decision-making, especially under conditions of limited access to the research object and a lack of information about its technical condition. The subject of this research is a previously unstudied issue: the development of a method for the urgent stability assessment of damaged multi-story buildings (DMBs) amid large-scale surveys, which will reduce the time for inspection, modeling, and decision-making regarding the reinforcement and reconstruction of a DMB or its dismantling. An important aspect of this method is forecasting the technical condition of DMBs using modern digital elements of artificial intelligence–neural networks. Optimizing the decision-making process under uncertainty is possible with the prior development of standard organizational and technological anti-emergency measures and methods for their application to typical DMB objects. Linking existing, pre-developed solutions using information and mathematical models of typical objects to a specific emergency DMB based on the pattern recognition principle allows for accelerating the selection of an option and ensuring the conduct of emergency rescue operations. In turn, this will help rescue potential victims, prevent accidents, and become part of the emergency response plan. In the post-war period, the use of the presented methodology will allow for the rapid assessment and forecasting of the technical condition of DMBs and the selection of an optimal strategy for their stabilization and reconstruction, including the frequency of monitoring needs and repair timelines. The application of neural networks, particularly hybrid models (CNNs, LSTMs, autoencoders), opens fundamentally new opportunities for shifting from a reactive to a proactive approach in assessing the technical condition of protective engineering structures (PES). The implementation of such technologies will enable automation of damage analysis, forecasting of damage progression, and the generation of well-grounded recommendations for repair, reinforcement, or demolition of structures. This significantly enhances the efficiency of decision-making and reduces risks for rescuers and engineers.
References
Popina, K., Reinhard, S., & Van Den Hoek, J. (2024, June 4). Scientists have calculated how many buildings in Ukraine were destroyed by the Russian army: The quantity is shocking. Dialog.ua. https://www.dialog.ua/ukraine/296077_1717492762
Komersant.info. (2024, May 2). Росія з початку великої війни зруйнувала та пошкодила понад 250 тисяч українських будинків (in Ukrainian). [Russia has destroyed and damaged over 250,000 Ukrainian homes since the beginning of the great war]. https://www.komersant.info/rosiia-z-pochatku-velykoi-viyny-zruynuvala-ta-poshkodyla-ponad-250-tysiach-ukrainskykh-budynkiv/
Hryhorovskyi, P., Osadcha, I., Jurelionis, A., Basanskyi, V., & Hryhorovskyi, A. (2022). A BIM-based method for structural stability assessment and emergency repairs of large-panel buildings damaged by military actions and explosions: Evidence from Ukraine. Buildings, 12(11), 1817. https://doi.org/10.3390/buildings12111817
TIMB. (2022, August 5). BIM Information Modeling Technologies in Construction. https://www.timb.org.ua/
Eskew, E., & Jang, S. (n.d.). Impacts and Analysis for Buildings under Terrorist Attacks. ResearchGate. Retrieved August 10, 2022, from https://www.researchgate.net/ publication/311517061_Impacts_and_Analysis_for_Buildings_under_Terrorist_Attacks
Suprun, M. (2022). The rocket-damaged high-rise on Chornobylskaya is being restored according to modern standards. Velykyi Kyiv. https://bigkyiv.com.ua/poshkodzhenu-raketoyu-bagatopoverhivku-na-chornobylskij-vidnovlyuyut-za-suchasnymy-standartamy/
Ministry of Development of Communities and Territories of Ukraine. (2022). Methods of inspection of buildings and structures damaged as a result of emergencies, hostilities, and acts of terrorism. Verkhovna Rada of Ukraine. https://zakon.rada.gov.ua/rada/ show/v0065914-22#Text
Grigorovskyi, P., Chervyakov, Y., Basanskyi, V., Kroshka, Y., Murasyova, O., & Chukanova, N. (2019). Information modeling of organizational and technological solutions of instrumental measurements in the creation and maintenance of construction objects. Construction Production Science Technology, 67, 7–16.
Mikhailenko, V., Rusan, I., Hryhorovskyi, P., Terentiev, O., Sviderskyi, A., & Horbatyuk, E. (2018). Models and methods of the information system for diagnosing the technical condition of construction objects. Comprint.
Trofymchuk O., & Kaliukh I. (2013). Activation of landslides in the south of Ukraine under the action of natural seismic impacts (experimental and analytical studies). Journal of Environmental Science and Engineering, 2(2), 68-76.
Kaliukh, I., Voloshkina, O., Efimenko, V., Sipakov, R., Zhukova, O., & Kaliukh, T. (2022, November). Modern technologies of Internet of Things in the restrained urban development for complicated ground conditions [Paper presentation]. 16th International Conference Monitoring of Geological Processes and Ecological Condition of the Environment, Kyiv, Ukraine. https://doi.org/10.3997/2214-4609.2022580086
Demenov, A., & Artamonov, A. (2015). Information modeling in the operation of buildings and structures. Science, 7, 1–9.
Ivanyk, I., Vikhot, S., Pozhar, R., Ivanyk, Y., & Vybranets, Y. (2010). Basics of reconstruction of buildings and structures. Publishing House of the National University “Lviv Polytechnic”.
Havrylyak, A. (2009). Basics of technical operation of buildings and engineering systems. Publishing House of the National University “Lviv Polytechnic”.
Adam, J. M., Parisi, F., Sagaseta, J., & Lu, X. (2018). Research and practice on progressive collapse and robustness of building structures in the 21st century. Engineering Structures, 173, 122–149. https://doi.org/10.1016/j.engstruct.2018.06.055
Izzuddin, B. A., Vlassis, A. G., Elghazouli, A. Y., & Nethercot, D. A. (2008). Progressive collapse of multi-storey buildings due to sudden column loss – Part I: Simplified assessment framework. Engineering Structures, 30(5), 1308–1318. https://doi.org/10.1016/j.engstruct.2007.07.011
Kokot, S., & Solomos, G. (2012). Progressive collapse risk analysis: Literature survey, relevant construction standards and guidelines. Joint Research Centre, European Commission.
Ellingwood, B. R. (2006). Mitigating risk from abnormal loads and progressive collapse. Journal of Performance of Constructed Facilities, 20(4), 315–323. https://doi.org/10.1061/(ASCE)0887-3828(2006)20:4(315
Ellingwood, B. R., & Dusenberry, D. O. (2005). Building design for abnormal loads and progressive collapse. Computer-Aided Civil and Infrastructure Engineering, 20(3), 194–205. https://doi.org/10.1111/j.1467-8667.2005.00387.x
Starossek, U., & Haberland, M. (2008, April 24-26). Measures of structural robustness–Requirements and applications [Paper presentation]. ASCE SEI 2008 Structures Congress–Crossing Borders, Vancouver, BC, Canada.
Starossek, U. (2007). Typology of progressive collapse. Engineering Structures, 29(9), 2302–2307. https://doi.org/10.1016/j.engstruct.2006.11.025
Chistyakov, E., Zenin, S., Sharipov, R., & Kudinov, O. (2017). The accounting of a deformability of structural discrete connections in calculation of constructive systems of large-panel buildings. Construction Science, 2, 123–127.
Ye, Z., Giriunas, K., Sezen, H., Wu, G., & Feng, D.-C. (2020). State-of-the-art review and investigation of structural stability in multi-story modular buildings. Journal of Building Engineering, 33, 10184. https://doi.org/10.1016/j.jobe.2020.101841
Inamdar, S. (2018). Joints and connections in precast concrete buildings. International Journal of Scientific Research, 7(6), 881–883. https://www.ijsr.net/archive/v7i6/ ART20183152.pdf
Gunawardena, T., & Mendis, P. (2022). Prefabricated building systems–Design and construction. Encyclopedia, 2(1), 70–95. https://doi.org/10.3390/encyclopedia2010007
Wang, W., Li, Z., Wu, Y., & Li, Y. (2020). Deep learning-based building damage detection from post-earthquake satellite imagery. Remote Sensing, 12(3), 350. https://doi.org/10.3390/rs12030350
Salehi, H., Bagheri, M., & Ghaffarian, S. (2019). Application of autoencoders in structural damage detection. Engineering Structures, 197, 109402. https://doi.org/10.1016/j.engstruct.2019.109402
Lin, T., Huang, Y., Sun, H., & Li, Y. (2021). Hybrid deep learning framework for structural health monitoring: CNN and LSTM fusion approach. Sensors, 21(3), 947. https://doi.org/10.3390/s21030947
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
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