Geomechanics and Engineering

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Prediction model of service life for tunnel structures in carbonation environments by genetic programming

  • Gao, Wei (Key Laboratory of Ministry of Education for Geomechanics and Embankment Engineering, College of Civil and Transportation Engineering, Hohai University) ;
  • Chen, Dongliang (Key Laboratory of Ministry of Education for Geomechanics and Embankment Engineering, College of Civil and Transportation Engineering, Hohai University)
  • Received : 2019.01.03
  • Accepted : 2019.06.23
  • Published : 2019.07.20
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Abstract

It is important to study the problem of durability for tunnel structures. As a main influence on the durability of tunnel structures, carbonation-induced corrosion is studied. For the complicated environment of tunnel structures, based on the data samples from real engineering examples, the intelligent method (genetic programming) is used to construct the service life prediction model of tunnel structures. Based on the model, the prediction of service life for tunnel structures in carbonation environments is studied. Using the data samples from some tunnel engineering examples in China under carbonation environment, the proposed method is verified. In addition, the performance of the proposed prediction model is compared with that of the artificial neural network method. Finally, the effect of two main controlling parameters, the population size and sample size, on the performance of the prediction model by genetic programming is analyzed in detail.

Keywords

Acknowledgement

Supported by : Central Universities

References

  1. Ahmad, S. (2003), "Reinforcement corrosion in concrete structures, its monitoring and service life prediction--a review", Cement Concrete Compos., 25(4-5), 459-471. https://doi.org/10.1016/S0958-9465(02)00086-0.
  2. Akpinar, P. and Uwanuakwa, I.D. (2016), "Intelligent prediction of concrete carbonation depth using neural networks", Bull. Transilvania Univ. Brasov, 9(58), 99-108.
  3. Ann, K.Y., Pack, S.W., Hwang, J.P., Song, H.W. and Kim, S.H. (2010), "Service life prediction of a concrete bridge structure subjected to carbonation", Construct. Build. Mater., 24(8), 1494-1501. https://doi.org/10.1016/j.conbuildmat.2010.01.023.
  4. Bu, N.R., Yang, G.L. and Zhao, H. (2009), "Prediction of concrete carbonization depth based on DE-BP neural network", Proceedings of 2009 Workshop on Intelligent Information Technology Applications, Nanchang, China, November.
  5. Do, N.A., Oreste, P., Dias, D., Antonello, C., Irini, Djeran-Maigre, and Livio, L. (2014), "Stress and strain state in the segmental linings during mechanized tunnelling", Geomech. Eng., 7(1), 75-85. https://doi.org/10.12989/gae.2014.7.1.075.
  6. Do, N.A., Dias, D. and Oreste, P. (2018), "Numerical investigation of segmental Tunnel linings-comparison between the hyperstatic reaction method and a 3D numerical model", Geomech. Eng., 14(3), 293-299. https://doi.org/10.12989/gae.2018.14.3.293.
  7. Feng, J.M. and Tian, M.Y. (2014), "A Study on the Classification and Grading of Environmental Effects on Tunnel Structure Durability", Modern Tunnel Technol., 51(3), 85-90 (in Chinese). https://doi.org/10.13807/j.cnki.mtt.2014.03.014
  8. FIB (International Federation for Structural Concrete) (2009), Structural Concrete: Textbook on Behaviour, Design and Performance, FIB, Lausanne, Switzerland.
  9. Gao, W. (2016), "Displacement back analysis for underground engineering based on immunized continuous ant colony optimization", Eng. Optimiz., 48(5), 868-882. https://doi.org/10.1080/0305215X.2015.1061814.
  10. Gao, W. and Yin, Z.X. (2011), Modern Intelligent Bionics Algorithm and Its Applications, Science Press, Beijing, China (in Chinese).
  11. Gokce, A., Koyama, F., Tsuchiya, M. and Gencoglu, T. (2009), "The challenges involved in concrete works of Marmaray immersed tunnel with service life beyond 100 years", Tunn. Undergr. Sp. Technol., 24(5), 592-601. https://doi.org/10.1016/j.tust.2009.01.001.
  12. Jesus, R.M.D., Collado, J.A.M., Go, J.L., Rosanto, M.A. and Tan, J.L. (2017), "Modelling of carbonation of reinforced concrete structures in intramuros, Manila using artificial neural network", Int. J. Geomate, 13(35), 87-92. https://doi.org/10.21660/2017.35.6683.
  13. Kari, O.P., Puttonen, J. and Skantz, E. (2014), "Reactive transport modelling of long-term carbonation", Cement Concrete Compos., 52(8), 42-53. https://doi.org/10.1016/j.cemconcomp.2014.05.003.
  14. Kellouche, Y., Boukhatem, B., Ghrici, M. and Tagnit-Hamou, A. (2019), "Exploring the major factors affecting fly-ash concrete carbonation using artificial neural network", Neural Comput. Appl., 31(S2), 969-988. https://doi.org/10.1007/s00521-017-3052-2.
  15. Koza, J.R. (1992), Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press, Cambridge, U.S.A.
  16. Lu, C.H. and Liu, R.G. (2009), "Predicting carbonation depth of prestressed concrete under different stress states using artificial neural network", Adv. Artificial Neural Syst., 1-8. https://doi.org/10.1155/2009/193139.
  17. Luo, D.M., Niu, D.T. and Dong, Z.P. (2014), "Application of Neural Network for Concrete Carbonation Depth Prediction", Proceedings of the 4th International Conference on the Durability of Concrete Structures, West Lafayette, Indiana, U.S.A., July.
  18. Miranda, T., Dias, D., Pinheiro, M. and Eclaircy-Caudron, S. (2015), "Methodology for real-time adaptation of Tunnels support using the observational method", Geomech. Eng., 8(2), 153-171. https://doi.org/10.12989/gae.2015.8.2.153.
  19. Nair, V. and Hinton, G.E. (2010), "Rectified linear units improve restricted Boltzmann machines", Proceedings of 27th International Conference on Machine Learning, Haifa, Israel, June.
  20. Pan, H.K. (2005), "Durability and reliability of underground structure under carbonation environment", Ph.D. Dissertation, Tongji University, Shanghai, China (in Chinese).
  21. Ranjith, A., Balaji Rao, K. and Manjunath, K. (2016), "Evaluating the effect of corrosion on service life prediction of RC structures-A parametric study", Int. J. Sustain. Built Environ., 5(2), 587-603. https://doi.org/10.1016/j.ijsbe.2016.07.001.
  22. Spyridis, P. (2014), "Adjustment of tunnel lining service life through appropriate safety factors", Tunn. Undergr. Sp. Technol., 40, 324-332. https://doi.org/10.1016/j.tust.2013.10.006.
  23. Sun, J. (2011), "Durability problems of lining structures for Xiamen Xiang'an subsea tunnel in China", J. Rock Mech. Geotech. Eng., 3(4), 289-301. https://doi.org/10.3724/sp.j.1235.2011.00289.
  24. Taffese, W.Z., Al-Neshawy, F., Sistonen, E. and Ferreira, M. (2015), "Optimized neural network based carbonation prediction model", Proceedings of the International Symposium Non-destructive Testing in Civil Engineering, Berlin, Germany, September.
  25. Taffese, W.Z. and Sistonen, E. (2013), "Service life prediction of repaired structures using concrete recasting method: State-ofthe-art", Procedia Eng., 57, 1138-1144. https://doi.org/10.1016/j.proeng.2013.04.143.
  26. Taffese, W.Z. and Sistonen, E. (2017), "Machine learning for durability and service-life assessment of reinforced concrete structures: Recent advances and future directions", Automat. Construct., 77, 1-14. https://doi.org/10.1016/j.autcon.2017.01.016.
  27. Taffese, W.Z., Sistonen, E. and Puttonen, J. (2015), "Prediction of concrete carbonation depth using decision trees", Proceedings of the 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium, April.
  28. Ventura, S. (2012), Genetic Programming - New Approaches and Successful Applications, InTech, Rijeka, Croatia.
  29. Woyciechowski, P.P. and Soko, J.J. (2017), "Self-terminated carbonation model as an useful support for durable concrete structure designing", Struct. Eng. Mech., 63(1), 55-64. https://doi.org/10.12989/sem.2017.63.1.055.
  30. Xiang, R. (2009), "Prediction of concrete carbonation depth based on support vector regression", Proceedings of the 2009 Workshop on Intelligent Information Technology Applications, Los Alamitos, California, U.S.A., November.
  31. Yuan, Y., Bai, Y. and Liu, J.H. (2012), "Assessment service state of tunnel structure", Tunn. Undergr. Sp. Technol., 27(1), 72-85. https://doi.org/10.1016/j.tust.2011.07.002.
  32. Zeng, X.M., Wang, Z.Y., Fan, J.Q., Zhao, L., Lin, D.L. and Zhao, J. (2011), "Problems of durability and reinforcement measures for underground structures in China", J. Rock Mech. Geotech. Eng., 3(3), 250-259. https://doi.org/10.3724/SP.J.1235.2011.00250.
  33. Zhang, J., Li, S.C., Li, L.P., Zhang, Q.Q., Xu, Z.H., Wu, J. and He, P. (2017), "Grouting effects evaluation of water-rich faults and its engineering application in Qingdao Jiaozhou Bay Subsea Tunnel, China", Geomech. Eng., 12(1), 35-52. https://doi.org/10.12989/gae.2017.12.1.035.

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