Computers and Concrete

  • 제31권4호
  • /
  • Pages.307-314
  • /
  • 2023
  • /
  • 1598-8198(pISSN)
  • /
  • 1598-818X(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Coupling numerical modeling and machine-learning for back analysis of cantilever retaining wall failure

  • 투고 : 2022.09.17
  • 심사 : 2023.02.09
  • 발행 : 2023.04.25
⟨ 이전 논문 다음 논문 ⟩

초록

In this paper we back-analyze a failure event of a 9 m high concrete cantilever wall subjected to earth loading. Granular soil was deposited into the space between the wall and a nearby rock slope. The wall segments were not designed to carry lateral earth loading and collapsed due to excessive bending. As many geotechnical programs rely on the Mohr-Coulomb (MC) criterion for elastoplastic analysis, it is useful to apply this failure criterion to the concrete material. Accordingly, the back-analysis is aimed to search for the suitable MC parameters of the concrete. For this study, we propose a methodology for accelerating the back-analysis task by automating the numerical modeling procedure and applying a machine-learning (ML) analysis on FE model results. Through this analysis it is found that the residual cohesion and friction angle have a highly significant impact on model results. Compared to traditional back-analysis studies where good agreement between model and reality are deemed successful based on a limited number of models, the current ML analysis demonstrate that a range of possible combinations of parameters can yield similar results. The proposed methodology can be modified for similar calibration and back-analysis tasks.

키워드

과제정보

The authors received no financial support for the research described in this paper.

참고문헌

  1. Agaiby, S. and Ahmed, S. (2016), "Learning from failures: A geotechnical perspective", International Conference on Forensic Civil Engineering, Nagpur, India, January.
  2. Alnaggar, M. and Bhanot, N. (2018), "A machine learning approach for the identification of the Lattice Discrete Particle Model parameters", Eng. Frac. Mech., 197, 160-175. https://doi.org/10.1016/j.engfracmech.2018.04.041.
  3. Ardiaca, D.H. (2009), "Mohr-Coulomb parameters for modelling of concrete structures", Plaxis Bull., 25, 12-15.
  4. Balki, I., Amirabadi, A., Levman, J., Martel, A.L., Emersic, Z., Meden, B., Garcia-Pedrero, A., Ramirez, S.C., Kong, D. and Moody, A.R. (2019), "Sample-size determination methodologies for machine learning in medical imaging research: A systematic review", Can. Assoc. Radiol. J., 70(4), 344-353. https://doi.org/10.1016/j.carj.2019.06.002.
  5. Breiman, L. (2001), "Random forests", Mach. Learn., 45(1), 5-32. https://doi.org/10.1023/A:1010933404324.
  6. Budhu, M. (2020), Soil Mechanics and Foundations, John Wiley & Sons, Hoboken, NJ, USA.
  7. Deb, D. and Das, K.C. (2010), "Extended finite element method for the analysis of discontinuities in rock masses", Geotech. Geol. Eng., 28, 643-659. https://doi.org/10.1007/s10706-010-9323-7.
  8. Dutta, S., Samui, P. and Kim, D. (2018), "Comparison of machine learning techniques to predict compressive strength of concrete", Comput. Concrete, 21(4), 463-470. https://doi.org/10.12989/cac.2018.21.4.463.
  9. Elmo, D. and Mitelman, A. (2021), "Modeling concrete fracturing using a hybrid finite-discrete element method", Comput. Concrete, 27(4), 297-304. https://doi.org/10.12989/cac.2021.27.4.297.
  10. Elmo, D., Mitelman, A. and Yang, B. (2022), "Examining rock engineering knowledge through a philosophical lens", Geosci., 12(4), 174. https://doi.org/10.3390/geosciences12040174.
  11. Geron, A. (2022), Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow, O'Reilly Media, Inc., Sebastopol, CA, USA.
  12. Jaky, J. (1948), "Pressure in soils", Proceedings of the 2nd International Conference on Soil Mechanics and Foundation Engineering, Rotterdam, The Netherlands, June.
  13. Kelleher, J.D. and Tierney, B. (2018), Data Science, MIT Press, Cambridge, MA, USA.
  14. Lees, A. (2013), Geotechnical Finite Element Analysis, ICE publishing, London, UK.
  15. Lelovic, S. and Vasovic, D. (2020), "Determination of MohrCoulomb parameters for modelling of concrete", Crystals, 10(9), 808. https://doi.org/10.3390/cryst10090808.
  16. Liu, L., Zhou, W. and Gutierrez, M. (2022), "Effectiveness of predicting tunneling-induced ground settlements using machine learning methods with small datasets", J. Rock Mech. Geotech. Eng., 14(4), 1028-1041. https://doi.org/10.1016/j.jrmge.2021.08.018.
  17. Mitelman, A. and Elmo, D. (2018), "A proposed probabilistic analysis methodology for tunnel support cost estimation depending on the construction method", 52nd US Rock Mechanics/Geomechanics Symposium, Seattle, WA, USA, June.
  18. Pul, S., Ghaffari, A., O ztekin, E., Husem, M. and Demir, S. (2017), "Experimental determination of cohesion and internal friction angle on conventional concretes", ACI Mater. J., 114(3), 407-416. http://doi.org/10.14359/51689676.
  19. Rao, V.V.S. and Babu, G.L.S. (2016), Forensic Geotechnical Engineering, Springer New Delhi, New Delhi, India.
  20. Rocscience Inc. (1998), Phase2, Rocscience Inc., Toronto, Canada.
  21. Rudin, C. (2019), "Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead", Nat. Mach. Intell., 1(5), 206-215. https://doi.org/10.1038/s42256-019-0048-x.
  22. Salazar, F. and Hariri-Ardebili, M.A. (2022), "Coupling machine learning and stochastic finite element to evaluate heterogeneous concrete infrastructure", Eng. Struct., 260, 114190. https://doi.org/10.1016/j.engstruct.2022.114190.
  23. Sherzer, G.L., Ye, G., Schlangen, E. and Kovler, K. (2022), "The role of porosity on degradation of concrete under severe internal and external brine attack in confined conditions", Constr. Build. Mater., 341, 127721. https://doi.org/10.1016/j.conbuildmat.2022.127721.
  24. Yarkoni, T. and Westfall, J. (2017), "Choosing prediction over explanation in psychology: Lessons from machine learning", Perspect. Psychol. Sci., 12(6), 1100-1122. https://doi.org/10.1177/1745691617693393.