Prediction model for permanent deformation of railway subgrade using an artificial neural network
DOI:
https://doi.org/10.14195/2184-8394_159_2Keywords:
Neural network, Permanent deformation, Predictive models, RailwayAbstract
The prediction of the permanent deformation in the subgrade and its reliability is one of the main concerns of the Railway Infrastructure Managers, as it can influence the reduction of the maintenance costs of the track in service. This study proposes a novel methodology for predicting permanent deformation based on a parametric study performed using a hybrid approach that includes the short and long term performance. The conducted study allowed the construction of a robust database used in this study to forecast the permanent deformation. The database feeds the neural network model, whose performance was evaluated using different metrics: MAE, MSE, RMSE, standard deviation, and regression coefficient. The model was tested and validated based on experimental results. The obtained results demonstrate that the developed model is rapid and efficient in accurately predicting the permanent deformation induced by the passage of trains. The model has the potential to be implemented in a computational decision support system for railway track maintenance and management.
Downloads
References
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.
