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I. Shardakov et al. / Procedia Structural Integrity 50 (2023) 257–265 Author name / Structural Integrity Procedia 00 (2019) 000 – 000

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Acknowledgements The study was supported by the Russian Science Foundation grant No. 22-19-00108, https://rscf.ru/project/22 19-00108/ References Brown, R.G. and Meyer, R.F. The Fundamental Theorem of Exponential Smoothing, Oper. Res. 9 (1961), 673 – 685. DOI: 10.1287/opre.9.5.673 Box G.E.P. et al. Time series analysis: Forecasting and control, 5th ed., John Wiley and Sons, New Jersey, 2015. Cui, Z.-D. and Ren, S.-X. Prediction of long-term settlements of subway tunnel in the soft soil area, Nat. Hazards. 74 (2014), 1007 – 1020. DOI: 10.1007/s11069-014-1228-y Glot, I. O., et al. Analysis of multiyear deformation processes in engineering structures based on the data from an automatic monitoring system, AIP Conference Proceedings 2053, 030019 (2018); https://doi.org/10.1063/1.5084380 Gusev, G. et al. The results of long-term observations of uneven settlements of buildings located on the territory of the Verkhnekamskoye potash deposit in Berezniki, News of the Ural State Mining University. 67 (2022), 80-89. DOI: 10.21440/2307-2091-2022-3-80-89 Holt, C.C. Forecasting seasonals and trends by exponentially weighted moving averages, Int. J. Forecast. 20 (2004), 5 – 10. DOI: 10.1016/j.ijforecast.2003.09.015. Hyndman, R.J. and Khandakar, Y. Automatic time series forecasting: The forecast package for R, J. Stat. Softw. 26 (2008), 1 – 22. DOI: 10.18637/jss.v027.i03 Hyndman, R.J. and Athanasopoulos, G. Forecasting: principles and practice, 2nd ed., OTexts, Melbourne, 2018. Ljung, G.M. et al. Box ’s contributions to time series analysis and forecasting, Appl. Stoch. Models Bus. Ind. 30 (2014), 25– 35. DOI: 10.1002/asmb.2016. Mazzanti P. Toward transportation asset management: what is the role of geotechnical monitoring?, J. Civ. Struct. Health Monit. 7 (2017), 645 – 656. DOI:10.1007/s13349-017-0249-0 Nie, L., et al. A new prediction model for mining subsidence deformation: the arc tangent function model, Nat. Hazards. 75 (2015), 2185 – 2198. DOI: 10.1007/s11069-014-1421-z Pardo, J.M. et al Instrumental monitoring of the subsidence due to groundwater withdrawal in the city of Murcia (Spain), Environ. Earth Sci. 70 (2016), 1957 – 1963. DOI: 10.1007/s12665-013-2710-7 Shardakov, I., et al. Control of surface subsidence based on building deformation monitoring data, MATEC Web Conf. 265 (2019), 05026 Shardakov, I.N., et al. The hydrostatic level method for continuous monitoring of building foundations, Solid State Phenom. 243 (2016), 105 – 111. DOI: 10.4028/www.scientific.net/SSP.243.105 Tang, Y. et al. Predicting settlement along railway due to excavation using empirical method and neural networks, Soils Found. 59 (2019), 1037 1051. DOI: 10.1016/j.sandf.2019.05.007 Yan, K. et al. Tunnel Surface Settlement Forecasting with Ensemble Learning, Sustainability. 12 (2020), 232-242. DOI: 10.3390/su12010232 Yang, B., et al. Time series analysis and long short-term memory neural network to predict landslide displacement, Landslides. 16 (2019), 677 694. doi:10.1007/s10346-018-01127-x Yang. N. and Bai, X. Forecasting structural strains from long-term monitoring data of a traditional Tibetan building, Struct. Control Health Monit. 26 (2019), e2300. DOI: 10.1002/stc.2300 Yepin, V.V., Tsvetkov, R.V. and Shardakov, I.N. Deformation monitoring of building foundations by hydrostatic leveling, Mag. Civ. Eng. 55 (2015), 93 – 94. DOI: 10.5862/MCE.55.3 Vorobev ,A.V. and Kashevarova, G.G. Adaptive models of short-term surface subsidence prediction and detection of the most hazardous building position in displacement trough, Mag. Civ. Eng. 44 (2013), 10 – 22. DOI: 10.5862/MCE.44.2. Winters, P.R. Forecasting Sales by Exponentially Weighted Moving Averages, Manag. Sci. 6 (1960), 324 – 342. DOI:10.1287/mnsc.6.3.324. Zhang X. and Zhang C. Automatic monitoring system for existing metro structural deformation induced by adjacent tunnel construction. Electron. J. Geotech. Eng. 21 (2016), 6727 – 6744.