Open Access
Issue
E3S Web Conf.
Volume 675, 2025
International Scientific Conference on Geosciences and Environmental Management (GeoME’5.5 2025)
Article Number 03010
Number of page(s) 11
Section Artificial Intelligence and Smart Modeling for Resilient Civil Infrastructure and Environmental Systems
DOI https://doi.org/10.1051/e3sconf/202567503010
Published online 11 December 2025
  1. N. C. Tsai, “A Review of Experimental Soil-Structure Interaction Damping”. [Google Scholar]
  2. G. Pandey, D. Patel, V. Mourya, R. Kumar, and S. Kumar, “A Review on Soil- Foundation-Interaction Models,” J. Rehabil. Civ. Eng., vol. 11, no. 3, Aug. 2023, doi: 10.22075/jrce.2022.25247.1570. [Google Scholar]
  3. D. E. Beskos, “Boundary Element Methods in Dynamic Analysis,” Appl. Mech. Rev., vol. 40, no. 1, pp. 1–23, Jan. 1987, doi: 10.1115/1.3149529. [Google Scholar]
  4. D. C. Rizos and Z. Wang, “Coupled BEM–FEM solutions for direct time domain soil– structure interaction analysis,” Eng. Anal. Bound. Elem., vol. 26, no. 10, pp. 877–888, Dec. 2002, doi: 10.1016/S0955-7997(02)00057-7. [Google Scholar]
  5. J. L. González Acosta, P. J. Vardon, and M. A. Hicks, “Development of an implicit contact technique for the material point method,” Comput. Geotech., vol. 130, p. 103859, Feb. 2021, doi: 10.1016/j.compgeo.2020.103859. [Google Scholar]
  6. C. Davidson et al., “Physical modelling to demonstrate the feasibility of screw piles for offshore jacket-supported wind energy structures,” Géotechnique, vol. 72, no. 2, pp. 108–126, Feb. 2022, doi: 10.1680/jgeot.18.P.311. [Google Scholar]
  7. T. Simpson, N. Dervilis, P. Couturier, N. Maljaars, and E. Chatzi, “Nonlinear Reduced Order Modelling of Soil Structure Interaction Effects via LSTM and Autoencoder Neural Networks,” Mar. 03, 2022, arXiv: arXiv:2203.01842. doi: 10.48550/arXiv.2203.01842. [Google Scholar]
  8. R. Swischuk, L. Mainini, B. Peherstorfer, and K. Willcox, “Projection-based model reduction: Formulations for physics-based machine learning,” Comput. Fluids, vol. 179, pp. 704–717, Jan. 2019, doi: 10.1016/j.compfluid.2018.07.021. [Google Scholar]
  9. D. Broc, “Soil-Structure Interaction: Theoretical and Experimental Results,” in Volume 4: Fluid Structure Interaction, Parts A and B, Vancouver, BC, Canada: ASMEDC, Jan. 2006, pp. 81–86. doi: 10.1115/PVP2006-ICPVT-11-93155. [Google Scholar]
  10. E. Korre, M. Zeghal, and T. Abdoun, “Liquefaction in the presence of soil-structure interaction: Centrifuge tests of a sheet-pile quay wall in LEAP-2020,” Soil Dyn. Earthq. Eng., vol. 181, p. 108650, Jun. 2024, doi: 10.1016/j.soildyn.2024.108650. [Google Scholar]
  11. K. AlKhatib, Y. M. A. Hashash, K. Ziotopoulou, and J. Heins, “Centrifuge and Numerical Modeling of the Seismic Response of Buried Water Supply Reservoirs,” J. Geotech. Geoenvironmental Eng., vol. 150, no. 3, p. 04023141, Mar. 2024, doi: 10.1061/JGGEFK.GTENG-11758. [Google Scholar]
  12. S. C. Jong, D. E. L. Ong, and E. Oh, “State-of-the-art review of geotechnical-driven artificial intelligence techniques in underground soil-structure interaction,” Tunn. Undergr. Space Technol., vol. 113, p. 103946, Jul. 2021, doi: 10.1016/j.tust.2021.103946. [Google Scholar]
  13. K. Gao, Z. Cheng, Y. Song, S. Yin, and Y. Chen, “Machine learning–driven surrogate model development for geotechnical numerical simulation,” Geotech. Res., pp. 1–14, Mar. 2025, doi: 10.1680/jgere.24.00029. [Google Scholar]
  14. S. M. Harle and R. L. Wankhade, “Machine learning techniques for predictive modelling in geotechnical engineering: a succinct review,” Discov. Civ. Eng., vol. 2, no. 1, p. 86, May 2025, doi: 10.1007/s44290-025-00224-w. [Google Scholar]
  15. B. Teodosio, P. L. P. Wasantha, E. Yaghoubi, M. Guerrieri, R. Van Staden, and S. Fragomeni, “Application of Artificial Intelligence in Reactive Soil Research: A Scientometric Analysis,” Geotech. Geol. Eng., vol. 43, no. 4, p. 145, Apr. 2025, doi: 10.1007/s10706-025-03097-z. [Google Scholar]

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