Modelo de previsão da deformação permanente de fundações de vias-férreas com recurso a uma rede neuronal artificial
DOI:
https://doi.org/10.14195/2184-8394_159_2Palavras-chave:
Redes neuronais, Deformação permanente, Modelos preditivos, Via férreaResumo
A previsão da deformação permanente na fundação e respetiva fiabilidade é uma das principais preocupações dos gestores das Infraestruturas Ferroviárias, pois pode influenciar os custos de manutenção da via em serviço. Este artigo propõe uma nova metodologia relativa à previsão da deformação permanente com base num estudo paramétrico realizado usando uma abordagem híbrida e que inclui o desempenho a curto e longo prazo. O estudo realizado permitiu a construção de uma base de dados robusta que foi utilizada neste estudo para prever a deformação permanente. A base de dados alimenta um modelo da rede neuronal, cujo desempenho foi avaliado com base em diferentes métricas: MAE, MSE, RMSE, desvio padrão e coeficiente de regressão. O modelo foi testado e validado com base em resultados experimentais. Os resultados obtidos mostram que o modelo desenvolvido é rápido e eficiente para prever com precisão a deformação permanente induzida pela passagem dos comboios. O modelo tem o potencial para ser implementado num sistema computacional de apoio de decisão para manutenção e gestão de linhas ferroviárias.
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Referências
Alves Costa, P.; Calçada, R.; Silva Cardoso, A. (2012). Track–ground vibrations induced by railway traffic: In-situ measurements and validation of a 2.5D FEM-BEM model. Soil Dynamics and Earthquake Engineering, 32, pp. 111-128. https://doi.org/10.1016/j.soildyn.2011.09.002.
Alves Costa, P.; Calçada, R.; Silva Cardoso, A.; Bodare, A. (2010). Influence of soil non-linearity on the dynamic response of high-speed railway tracks. Soil Dynamics and Earthquake Engineering, 30, pp. 221-235. https://doi.org/10.1016/j.soildyn.2009.11.002.
Chen, R.; Chen, J.; Zhao, X.; Bian, X.; Chen, Y. (2014). Cumulative settlement of track subgrade in high-speed railway under varying water levels. International Journal of Rail Transportation, 2, pp. 205–220. https://doi.org/10.1080/23248378.2014.959083.
EN13848-5 (2008). Railway Applications - Track - Track geometry quality - Part 5: Geometric quality levels. EN13848. European Committee for Standardization (CEN), Brussels, Belgium.
Garson, G. D. (1991). Interpreting neural network connection weights. Artificial Intelligence Expert, 6, pp. 46–51.
Ghorbani, B.; Arulrajah, A.; Narsilio, G.; Horpibulsuk, S.; Bo, M. W. (2021). Shakedown analysis of PET blends with demolition waste as pavement base/subbase materials using experimental and neural network methods. Transportation Geotechnics, 27. https://doi.org/10.1016/j.trgeo.2020.100481.
Gomes Correia, A.; Ramos, A. (2022). A geomechanics classification for the rating of railroad subgrade performance. Railway Engineering Science, 30, pp. 323-359. https://doi.org/10.1007/s40534-021-00260-z.
Hanandeh, S.; Ardah, A.; Abu-Farsakh, M. (2020). Using artificial neural network and genetics algorithm to estimate the resilient modulus for stabilized subgrade and propose new empirical formula. Transportation Geotechnics, 24. https://doi.org/10.1016/j.trgeo.2020.100358.
Indraratna, B.; Armaghani, D. J.; Gomes Correia, A.; Hunt, H. & Ngo, T. (2023). Prediction of resilient modulus of ballast under cyclic loading using machine learning techniques. Transportation Geotechnics, 38. https://doi.org/10.1016/j.trgeo.2022.100895.
Ling, X.; Li, P.; Zhang, F.; Zhao, Y.; Li, Y.; An, L. (2017). Permanent deformation characteristics of coarse grained subgrade soils under train-induced repeated load. Advances in Materials Science and Engineering, 2017, pp 1-15. https://doi.org/10.1155/2017/6241479.
Lopes, P.; Alves Costa, P.; Ferraz, M.; Calçada, R.; Silva Cardoso, A. (2014). Numerical modeling of vibrations induced by railway traffic in tunnels: From the source to the nearby buildings. Soil Dynamics and Earthquake Engineering, 61–62, pp. 269–285. https://doi.org/10.1016/j.soildyn.2014.02.013.
Nielsen, J. C. O.; Li, X. (2018). Railway track geometry degradation due to differential settlement of ballast/subgrade – Numerical prediction by an iterative procedure. Journal of Sound and Vibration, 412, pp. 441-456. https://doi.org/10.1016/j.jsv.2017.10.005.
Olden, J. D.; Joy, M. K.; Death, R. G. (2004). An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data. Ecological Modelling, 178, pp. 389-397. https://doi.org/10.1016/j.ecolmodel.2004.03.013.
Pahno, S.; Yang, J.; Kim, S. (2021). Use of Machine Learning Algorithms to Predict Subgrade Resilient Modulus. Infrastructures, 6, pp. 1-16. https://doi.org/10.3390/infrastructures6060078.
Ramos, A.; Gomes Correia, A.; Calçada, R.; Alves Costa, P. (2021a). Stress and permanent deformation amplification factors in subgrade induced by dynamic mechanisms in track structures. International Journal of Rail Transportation, 10, pp. 298-330. https://doi.org/10.1080/23248378.2021.1922317.
Ramos, A.; Gomes Correia, A.; Calçada, R.; Alves Costa, P.; Esen, A.; Woodward, P. K.; Connolly, D. P.; Laghrouche, O. (2021b). Influence of track foundation on the performance of ballast and concrete slab tracks under cyclic loading: Physical modelling and numerical model calibration. Construction and Building Materials, 277. https://doi.org/10.1016/j.conbuildmat.2021.122245.
Ramos, A.; Gomes Correia, A.; Indraratna, B.; Ngo, T.; Calçada, R.; Costa, P. A. (2020). Mechanistic-empirical permanent deformation models: Laboratory testing, modelling and ranking. Transportation Geotechnics, 23. https://doi.org/10.1016/j.trgeo.2020.100326.
Ramos, A. L.; Correia, A. G.; Calçada, R.; Costa, P. A. (2018). Influence of permanent deformations of substructure on ballasted and ballastless tracks performance. Proceedings of 7th Transport Research Arena TRA 2018, April 16-19, 2018 Vienna, Austria. Zenodo.
Rezaei-Tarahomi, A.; Kaya, O.; Ceylan, H.; Kim, S.; Gopalakrishnan, K.; Brill, D. R. (2017). Development of rapid three-dimensional finite-element based rigid airfield pavement foundation response and moduli prediction models. Transportation Geotechnics, 13, pp. 81-91. https://doi.org/10.1016/j.trgeo.2017.08.011.
Sadri, M.; Lu, T.; Zoeteman, A.; Steenbergen, M. (2018) Railway track design & degradation. In: Dimitrovová, Z., ed. MATEC Web of Conferences, Lisbon, Portugal.
Sheng, X.; Jones, C. J. C.; Thompson, D. J.( 2006). Prediction of ground vibration from trains using the wavenumber finite and boundary element methods. Journal of Sound and Vibration, 293, pp. 575-586. https://doi.org/10.1016/j.jsv.2005.08.040.
Sykora, D. W. (1987). Creation of a data base of seismic shear wave velocities for correlation analysis. Geotech. Lab. Miscellaneous Paper GL-87-26, U.S. Army Eng.
Tinoco, J., Correia, A.; Cortez, P.; Toll, D. (2019). Artificial Neural Networks for Rock and Soil Cutting Slopes Stability Condition Prediction: Proceedings of the 2nd GeoMEast International Congress and Exhibition on Sustainable Civil Infrastructures, Egypt 2018 – The Official International Congress of the Soil-Structure Interaction Group in Egypt (SSIGE). https://doi.org/10.1007/978-3-030-02032-3_10.
Tinoco, J.; Gomes Correia, A.; Cortez, P. (2014). Support vector machines applied to uniaxial compressive strength prediction of jet grouting columns. Computers and Geotechnics, 55, pp. 132-140. https://doi.org/10.1016/j.compgeo.2013.08.010.
Tinoco, J.; Gomes Correia, A.; Cortez, P. (2018). Jet grouting column diameter prediction based on a data-driven approach. European Journal of Environmental and Civil Engineering, 22, pp. 338-358. https://doi.org/10.1080/19648189.2016.1194329.
Yang, Y.; Hung, H. (2001). A 2.5D finite/infinite element approach for modelling visco-elastic body subjected to moving loads. International Journal for Numerical Methods in Engineering, 51, pp. 1317-1336. https://doi.org/10.1002/nme.208.
